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Adaptation in Natural and Arti cial Systems
... Often principles of nature are a role model for such approaches. One example are evolution-based methods such as genetic programming [14]. Another example are behavior-based techniques such as Reinforcement Learning, which seem to be an appropriate approach to our problem of object learning. ...
... This yields an error for a reconstructed view. In the example in fi gure 7 the non-acquired view (7, 11) is reconstructed from the key views (3,7) and (14,7). It can be compared to the original view (7,11). ...
... This technique is used for the calculation of the reward signal after each step of a scan episode as well as for the calculation of the total reconstruction error after a scan path has been learned. original view (14,7) morphed view (7,11) original view (7, 11) ...
W e propose an architectural model for a responsive vision system based on techniques of reinforcement learning. It is capable of acquiring object representations based on the intended application. The system can be interpreted as an intelligent scanner that interacts with its environment in a perception-action cycle, choosing the camera parameters for the next view of an object depending on the information it has perceived so far. The main contribution of this paper consists in the presentation of this general architecture which can be used for a variety of applications in computer vision and computer graphics. In addition, the funcionality of the system is demonstrated with the example of learning a sparse, view-based object representation that allows for the reconstruction of non-acquired views. First results suggest the usability of the proposed system.
... The best performing software package is expected to nd the best alignment statistically for the uncharacterized protein sequences. Holland (1975) 16 introduced a genetic algorithm commonly applied to optimization problems arising in science and engineering. The algorithm uses the three principles of evolution, namely, mutation, crossover and natural selection to solve an optimization problem. ...
... The best performing software package is expected to nd the best alignment statistically for the uncharacterized protein sequences. Holland (1975) 16 introduced a genetic algorithm commonly applied to optimization problems arising in science and engineering. The algorithm uses the three principles of evolution, namely, mutation, crossover and natural selection to solve an optimization problem. ...
Certain functional, structural and evolutionary relationships among the protein sequences can be inferred by protein Multiple Sequence Alignment (MSA). There are many algorithms developed over three decades but all have inherent limitations. Two important protein sequence alignment programs, ProbCons and Mcoffee stand out. Here the two best MSA algorithms are used to create more efficient computer programs through machine learning approach. The Darwinian principles of evolution are used. The evolutionary operators of a genetic algorithm such as mutation, crossover and selection are implemented to find the optimized protein sequence alignment after several iterations of the algorithm. Thus, we have developed a new MSA computational tool called Protein Alignment by Stochastic Algorithm (PASA). The efficiency of protein sequence alignments is evaluated in terms of the Total Column (TC) score. The TC score is basically the number of correctly aligned columns between the test alignments and the reference alignments divided by the total number of columns. The PASA is found to be statistically more accurate protein alignment method in comparison to other popular bioinformatics tools including ProbCons and Mcoffee.
... Genetic algorithms (GAs) are stochastic optimization algorithms based on the concepts of biological evolutionary theory (Goldberg, 1989;Holland, 1975). They consists in maintaining a population of chromosomes (individuals), which represent potential solutions to the problem to be solved, that is, the optimization of a function, generally very complex. ...
... Selection mechanism. The selection process of stochastic sampling with replacement is used to create the intermediate population P H (Goldberg, 1991;Holland, 1975). ...
... El programa teórico de este trabajo se vincula con la teoría de los sistemas complejos (Kauffman, 1993;Holland, 1975Holland, , 2006Cancho, Janssen, y Solé, 2001;Frenken, 2006;Arthur, 2009), así como con la teoría de economía de la innovación (Valverde y otros, 2007;Fleming y Sorenson, 2001). ...
... A pesar de que una variedad extensa de fenómenos de la naturaleza son sistemas complejos, el estudio de éstos sólo tiene unas cuatro décadas (Mitchell, 2009). Los análisis y formas de representación han sido diversos y complementarios; incluyen el estudio de fractales (Maldenbrot, 1983), la modelación basada en agentes (Conway, 1970;Nowak y May, 1992), el desarrollo y aplicación del algoritmo genético (Holland, 1975; y la teoría de redes (Barabasí y Reka, 2002;Newman, 2003), entre otros. Una de las representaciones más utilizadas para analizar la complejidad ha sido el modelo Nk de Kauffman (1993) y la generalización de Altenberg (1994), aplicada en genética, ciencias de la computación e innovación tecnológica (Frenken, 2006). ...
Text in Spanish. Analysis of the autoparts-automobile global value chain and specifically of US-imports from Mexico and its main segments during 1990-2012.
... Genetic algorithms (GA) were proposed in 1950s and 1960s under the shadow of the idea that natural evolution can be imitated and be used as a tool to tackle optimization problems [7,8]. Holland [9] mentions that, many living organisms evolve in two cases, which are natural selection and sexual reproduction. Natural selection ensures the organisms that will survive considering their test of fitness such as recognizing a predator and fleeing. ...
... In this paper we have used, roulette wheel selection, and tournament selection that Larranaga et al. [15], Rani and Kumar [16], and Zakir [12] have used in their studies. a) Roulette Wheel Selection: When Holland [9] offered the genetic algorithm, he preferred fitness proportionate selection by dividing the fitness of chromosome by the average of fitness of the population. This way, the probability of an individual to be selected will be directly proportional to the fitness of the individual. ...
... Genetic algorithms are random global search and optimization methods [23] that describe biological evolution using three operators: replication, crossover, and mutation. The GA generates a set of solutions for each iteration. ...
Under the same geological conditions, the thickness and length of the reinforced strip, the slope ratio of the reinforced embankment, the modulus of elasticity of the fill and the reinforced strip, and the friction angle at the interface between the reinforcement and the soil, are the main design parameters that have an important influence on the stress, deformation, and stability of the encompassing reinforced soil embankment. To quickly and accurately determine the optimal design parameters for reinforced soil embankments with wrapped faces, ensuring minimal cost, while maintaining structural safety, we propose a design parameter prediction model based on a GA–BP neural network. This model evaluates parameters within their specified ranges, using maximum lateral displacement, maximum vertical displacement, maximum stress in the XZ direction, the maximum shear strain increment, and the safety factor, as assessment criteria. The primary objective is to minimize the overall cost of the embankment. A comparison with five machine learning algorithms shows that the model has high prediction accuracy, and the optimal design parameter combinations obtained from the optimization search can significantly reduce the cost of the embankment, while controlling the displacement and stability of the embankment. Therefore, the GA–BP network is suitable for predicting the optimal design parameters of reinforced soil embankments with wrapped faces.
... The Genetic Algorithm (GA) derives its concept from evolutionary concepts. These principles were embraced as a computational method for optimization in solving problems (Holland, 1975;Jong, 1975). The genetic algorithm (GA) is based on the movement of genes in a chromosomal population; strings of ones and zeros represent genes. ...
The paucity of energy orchestrated by the demerits of non-renewable energy sources has posed significant challenges to global demand for energy. Salvaging this difficulty, geothermal renewable energy resource is considered as an alternative. This study investigated the effectiveness of a GIS-based machine learning algorithm to analyze remote sensing and geophysical datasets to address this task. The acquired remote sensing dataset was processed to derive surface-induced geothermal conditioning factors (GCFs): land use land cover, normalized difference vegetation index, lineament density, land surface temperature, and slope percent. Spectral analysis was carried out on aeromagnetic data to derive sub-surface induced GCFs, including Curie point depth, heat flow, and geothermal gradient. Geospatial analysis module, the thematic maps for the GCFs were produced in a GIS environment. With MATLAB program coding, the optimized weights for the produced GCFs thematic maps were determined using machine learning algorithms by applying adaptive neuro-fuzzy inference system (ANFIS) models incorporated with metaheuristic optimization mechanisms. The output of various metaheuristic optimization algorithms, such as Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Particle Swarm Optimization (PSO), was processed in the GIS platform to create maps of the geothermal potential predictive index (GPPI) for the study area. Receiver Operating Characteristics (ROC) curve, Root Mean Square Error (RMSE), and multifaceted geology were used to validate the produced GPPI model maps. The multifaceted geology approach validation results revealed that a high probability of geothermal manifestation predominates the Ifewara shear zone. The results of the ROC-AUC on the optimized ANFIS models, namely: IWO-ANFIS, GA-ANFIS, and PSO-ANFIS, are 77.2%, 81.1%, and 77.5%, respectively, compared to 73.5% from the ANFIS model. The observed RMSE validation results also determined the prediction values of 0.041803, 0.10281, and 0.10734 for IWO-ANFIS, GA-ANFIS, and PSO-ANFIS compared to 0.021803 for the conventional ANFIS model. The GA-ANFIS model performed better than all investigated machined learning algorithm models. The study shows that the GPPI model map created by GA-ANFIS could be employed for accurate decision-making in geothermal resource exploitation in the investigated area and other regions with comparable geologic terrain.
... A number of research grade chemistry [6] [49] and electrode options [35] are being investigated to improve current capacitor storage however no transformationally new power storage technology has commercialised successfully since Lithium-ion. Advancements in design and materials are being sought to increase the density of power storage and the preservation of capacitance across wider temperature ranges [6] [74] and battery ages [9] and currently activated carbon technology presents the highest potential for both [32] and an ideal material for use in space. ...
To date only single vehicle thrust designs have been considered for propulsion in space, however there is no limitation preventing multivehicle distributed thrust application designs. By considering the regularity of planetary orbits and distance between orbital paths, a location in space can be targeted for next orbit arrival of a shipping container propelled on an intercepting vector that is a result of induction propulsion vector combination. This concept is achieved with Nb 3 Sn high energy pulse solenoids that enable a square pyramidal swarm of satellites to decompose the intercepting orbital transfer vector and propel a 20 tonne steel shipping container towards a destination swarm. This paper is a thought experiment with first pass mechanical analysis that establishes a validity algorithm then concludes that the proposed yoked solenoid design is fit for purpose and the concept is theoretically functional. The launch vector is perpendicular to the swarm’s orbital path thus force application, reaction and orbital maintenance strategies are investigated. A yoke rod’s enhancement of a superconductive solenoid’s force output and the reaction force distribution model are noted as key variances that must be investigated in further research with numerical modelling tools before the proposed design can be considered fully functional and viable for experimental testing. This design is a dedicated freight transport solution to high mass, high volume cargo required by future astronauts, such as construction machinery and materials.
... The implementation of an EC algorithm is expected to inherit from the super-class named SearchAlgorithm. EvoTorch provides the following ready-to-use EC algorithms: exponential and separable variations of natural evolution strategies (XNES [Glasmachers et al., 2010] and SNES [Schaul et al., 2011]), policy gradients with parameter-based exploration (PGPE [Sehnke et al., 2010]), cross entropy method (CEM [Rubinstein, 1997[Rubinstein, , 1999), covariance matrix adaptation evolution strategy (CMA-ES [Hansen and Ostermeier, 2001]), general genetic algorithms [Holland, 1975[Holland, , 1992, non-dominated sorting genetic algorithm (NSGA-II [Deb et al., 2002]), cooperative synapse neuroevolution (CoSyNE [Gomez et al., 2008]), and multi-dimensional archive of phenotypic elites (MAP-Elites [Mouret and Clune, 2015]). ...
Evolutionary computation is an important component within various fields such as artificial intelligence research, reinforcement learning, robotics, industrial automation and/or optimization, engineering design, etc. Considering the increasing computational demands and the dimensionalities of modern optimization problems, the requirement for scalable, re-usable, and practical evolutionary algorithm implementations has been growing. To address this requirement, we present EvoTorch: an evolutionary computation library designed to work with high-dimensional optimization problems, with GPU support and with high parallelization capabilities. EvoTorch is based on and seamlessly works with the PyTorch library, and therefore, allows the users to define their optimization problems using a well-known API.
... Genetic Algorithm was first invented by Holland [8] in the 1970s to mimic the evolution that happens in nature. This technique is achieved by generating a number of random individuals (called initial population); each consists of randomly chosen string of parameters to be optimized (called genes) and each individual is given a parameter that measures its proximity to the objective of the optimization process. ...
... Les algorithmes génétiques (AG) ont été largement utilisés comme une approche fonctionnelle pour résoudre diérents problèmes d'optimisation. Le concept d'algorithme génétique a été introduit par Holland et al. [80] comme une technique qui imite le processus de l'évolution biologique des espèces pour résoudre des problèmes combinatoires. Cette méthode approchée a été utilisée pour résoudre diérents types de problèmes d'ordonnancement : par exemple, nous pouvons citer Jawahar et al. [86] qui ont décrit un AG pour générer une combinaison optimale de règles de priorité de lancement de production pour obtenir la planication d'un système d'atelier exible. ...
L’évolution continue des environnements de production et l’augmentation des besoins des clients, demandent un processus de production plus rapide et efficace qui contrôle plusieurs paramètres en même temps. Nous nous sommes intéressés au développement de méthodes d’aide à la décision qui permettent d’améliorer l’ordonnancement de la production. L’entreprise partenaire (Norelem) fabrique des pièces de précision mécanique, il faut donc prendre en compte les différentes contraintes de ressources (humaines et d’outillage) existantes dans l’atelier de production.Nous avons abordé l’étude d’un atelier d’ordonnancement de type open shop ou chemin ouvert, où une tâche peut avoir de multiples séquences de production puisque l’ordre de fabrication n’est pas fixé et l’objectif à minimiser est le temps total de séjour. Des contraintes d’affectation de ressources humaines (multi-compétences) et de disponibilité d’outillage ont été prises en compte.Des modèles mathématiques linéaires et non-linéaires ont été développés pour décrire la problématique. Etant donné que les méthodes exactes sont limitées aux instances de petites tailles à cause des temps de calcul, des méthodes de résolution approchées ont été proposées et comparées. De plus, nous avons abordé l’optimisation multi-objectif en considérant trois objectifs, la minimisation du temps total de séjour et l’équilibrage de charge des ressources (humaines et machines).L’efficacité des méthodes est prouvée grâce à des tests sur des instances théoriques et l’application au cas réel
... Selection based on fitness value leads the search to prematurely converge. However Linear ranking [7] and Tournament selection [8] have been proposed to check these problems. ...
In recent years search based optimization techniques in software engineering has been a burgeoning interest among software engineering. The Search Based Optimization Techniques are used to shift problem of software optimization from human based search to machine based search, by using techniques like meta-heuristic search and evolutionary computation. The idea behind this paradigm is to mingle human creativity with computing machine's reliability. This article presents a survey on some good work already done in this field.
... Strategy area has observed pro-cyclical processes induced by reducing variation and autonomous processes that increase variation (Burgelman, 1991); and in managerial economics, static e ciency and dynamic e ciency (Ghemawat & Ricart Costa, 1993). discusses exploitation and exploration from the viewpoint of adaptive process studies -the relationship between exploring new opportunities (exploration) or exploring old certainties (exploitation) -, a view that has fundamentals in Schumpeter (1934) and Holland (1975). ...
Context: ambidexterity is a dynamic capability that seeks to balance exploitation and exploration initiatives. The joint development of exploitation and exploration can be achieved through dynamic ambidexterity. Theoretical discussions involving the relationship between the concepts of ambidexterity and dynamic capabilities (DCs) have already been developed in literature. However, the way the three ambidextrous approaches (structural, contextual, and sequential) are based on DCs still needs to be observed by researchers. Objective: this study aims to propose a conceptual and theoretical hypothetical model that explains the influence of various types of organizational ambidexterity (structural, contextual, and sequential) on the development of DCs and their relation to organizational performance. Methodology: the study was developed through an extensive systematic literature review guided by an inductive logic, interpretive epistemology, and qualitative approach. Results: the analyses and discussions made it possible to present a theoretical hypothetical model of dynamic ambidexterity that involves nine constructs and eleven hypotheses. Conclusion: we believe that our study contributes theoretically to the field of organizational Strategies and can enable studies aligned with the concepts of dynamic ambidexterity and DCs.
... In order to improve the prediction accuracy of BP neural network on the original basis, this paper adopts genetic algorithm (GA) to optimize it. The genetic algorithm was originally proposed by Professor Holland of Michigan University [30], which is a method to simulate the biological evolution mechanism of nature, that is, useful retention and useless removal in the optimization process. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can quickly get better optimization results. ...
This paper proposed a modular tide level prediction model based on nonlinear autoregressive exogenous model (NARX) neural network in order to improve the accuracy of tide prediction. The model divides tide data into two parts: the astronomical tide data affected by celestial tide generating force, and non-astronomical tide data affected by various environmental factors. NARX neural network and harmonic analysis are used to simulate and predict the non-astronomical and astronomical part of tide respectively, and then the final result is obtained by combining the two parts. In this paper, the tide data from Yorktown, USA, are used to simulate the prediction of tide level, and the results are compared with the traditional harmonic analysis (HA) method and Genetic Algorithm-Back Propagation (GA-BP) neural network. The results show that as a dynamic neural network, NARX neural network modular prediction model is more suitable for the analysis and prediction of time series data and has better stability and accuracy.
... By using the Lamarckian genetic algorithm (LGA) [58], we performed molecular docking. In molecular docking through genetic algorithms (GA) [59], the particular arrangement of a ligand and a protein can be de ned by a set of values describing the translation, orientation, and conformation of the ligand with respect to the protein. All thigh pro les were produced under the following conditions: an initial population of 150 randomly placed individuals and a maximum number of 2.5 × 106 energy evaluations, a maximum number of 27,000 generations, a mutation rate of 0.02, a crossover rate of 0.80, and an elitism value of 1. Finally, results were clustered and analyzed considering binding energies and main interacting residues in each complex by bioinformatics module of ICM 3.7.3 ...
Protein products of SARS-CoV-2 spike (S) coding gene sequence, were all analyzed and compared to other SARS-CoV S proteins to elucidate structural similarities of spike proteins. A homology modeling of SARS-CoV-2 S protein was obtained and used in molecular docking studies to find binding affinities of spike protein for angiotensin-converting enzyme 2 (ACE2). The two most important binding sites of S protein, namely, RBD and CTD, critically responsible for binding interactions, were identified. Finally, binding affinity of RBD and CTD domains of S protein with narcotic analgesics are studied. Moreover, interactions of ACE2 receptor- S protein with narcotic compounds when mixed with small molecule adjuvants to improve the immune response and increase the efficacy of potential vaccines, were taken into consideration. In-silico results suggest that the combination of narcotine hemiacetal with mannide monooleate shows a stronger binding affinity with CTD, while carprofen-muramyl dipeptide and squalene have stronger binding affinities for the RBD portion of S protein. Thus, a suitable combination of these narcotic is proposed to yield potent site-blocking efficacy for ACE2 receptor against SARS-CoV-2 spike proteins.
... By using the Lamarckian genetic algorithm (LGA) [56], we performed molecular docking. In molecular docking through genetic algorithms (GA) [57], ...
Protein products of SARS-CoV-2 spike (S) coding gene sequence, were all analyzed and compared to other SARS-CoV S proteins to elucidate structural similarities of spike proteins. A homology modeling of SARS-CoV-2 S protein was obtained and used in molecular docking studies to find binding affinities of spike protein for angiotensin-converting enzyme 2 (ACE2). The two most important binding sites of S protein, namely, RBD and CTD, critically responsible for binding interactions, were identified. Finally, binding affinity of RBD and CTD domains of S protein with narcotic analgesics are studied. Moreover, interactions of ACE2 receptor- S protein with narcotic compounds when mixed with small molecule adjuvants to improve the immune response and increase the efficacy of potential vaccines, were taken into consideration. In-silico results suggest that the combination of narcotine hemiacetal with mannide monooleate shows a stronger binding affinity with CTD, while carprofen-muramyl dipeptide and squalene have stronger binding affinities for the RBD portion of S protein. Thus, a suitable combination of these narcotic is proposed to yield potent site-blocking efficacy for ACE2 receptor against SARS-CoV-2 spike proteins.
... Эволюционные алгоритмы -это обширный подкласс стохастических методов оптимизации, как правило, взаимодействующих с оптимизируемой функцией как с «черным ящиком». Под эволюционными алгоритмами в узком смысле чаще всего подразумевают генетические алгоритмы [1], эволюционные стратегии [2,3], эволюционное программирование [4] и генетическое программирование [5]. В широком смысле данный термин также включает в себя методы роевого интеллекта [6][7][8], методы на основе физических моделей [9,10], а также методы, разработанные без привлечения каких-либо аналогий с природными процессами или явлениями [11][12][13]. ...
Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ,λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation.We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear function with random weights, as well as on random satisfiable MAX-3SAT problems.
... Une repr esentation \naturelle" d'une forme contenue dans un domaine de travail r egulier passe donc par la discr etisation de ce domaine, puis par l'attribution a chaque el ement de cette discr etisation d'une valeur mati ere ou solide, i.e. 0 ou 1. Or il se trouve que cette repr esentation semble egalement entrer parfaitement dans le cadre historiques des algorithmes g en etiques 32,22]. Ceci explique que tous les travaux ant erieurs utilisant les m ethodes stochastiques pour les mêmes types de probl emes ( 31,21,37,15,41]) aient utilis e cette repr esentation. ...
Le travail présenté ici concerne l'optimisation sous contraintes dans le contexte de l'optimisation de formes en Mécanique des Solides. Nous nous restreindrons aux méthodes d'optimisation stochastiques que sont les Algorithmes Génétiques.
... In the family of meta-heuristics, another important type of algorithm is the Genetic Algorithm (GA) [91]. It consists in mimicking the process of evolution of living organisms: here again, it involves several individuals. ...
The bottleneck of today’s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this work, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Several tests were performed, one on an artificial problem designed specifically to test this approach and the three others on instances taken from the literature. The obtained results are satisfying with regard to the current state of air operations management and constraints.
... The combination of genetic algorithms (GA) and BL heuristic have been the prevalent approach for C&P problems [23], [28], [29]. GA are search algorithms inspired by the natural selection processes, in which a population of individuals evolve through selection and reproduction of the fittest individuals, which transmit their genetic characteristics to the next generation of individuals [30]. The literature contains several examples of packing techniques based on such an approach and employed into real-world problems like circuit design and 3D Printing. ...
The irregular strip packing problem (ISPP) is a combinatorial optimisation problem that has applicability in several industrial processes since it aims for the efficient use of material. Most of the techniques reported in the literature for solving the ISPP employ metaheuristics as they can cope with complex requirements that prevent the use of exact model formulations. This paper presents a biased random-key genetic algorithm (BRKGA) that uses the dotted board model to compute the fitnesses of candidate solutions aiming for the minimisation of the height of the large object. The algorithm allows the pieces to rotate in order to achieve better layouts. Computational experiments using instances from the literature were conducted to demonstrate the efficiency of the proposed method, with promising results. Index Terms-cutting and packing, biased random-key genetic algorithm, linear programming.
... Genetic algorithms are part of evolutionary algorithms inspired by the theory of evolution [46][47][48]. ...
... 2,6 The most widely used metaheuristic algorithm is the genetic algorithm (GA). 7,8 GAs have been shown to be valuable tools for solving complex optimization problems in wide range of¯elds, including the¯eld of water resources. [9][10][11][12][13][14] Other metaheuristic algorithms such as Simulated Annealing (SA), Ant Colony (AC), Di®erential Evolution (DE), Particle Swarm Optimization (PSO), Harmony Search (HS), etc., have also been applied to solve the water resources problems, although their applications are much more limited than those of GAs. ...
Groundwater management problems are typically of a large-scale nature, involving complex nonlinear objective functions and constraints, which are commonly evaluated through the use of numerical simulation models. Given these complexities, metaheuristic optimization algorithms have recently become popular choice for solving such complex problems which are difficult to solve by traditional methods. However, the practical applications of metaheuristics are severely challenged by the requirement of large number of function evaluations to achieve convergence. To overcome this shortcoming, many new metaheuristics and different variants of existing ones have been proposed in recent years. In this study, a recently developed algorithm called flower pollination algorithm (FPA) is investigated for optimal groundwater management. The FPA is improved, combined with the widely used groundwater flow simulation model MODFLOW, and applied to solve two groundwater management problems. The proposed algorithm, denoted as IFPA, is first tested on a hypothetical aquifer system, to minimize the total pumping to contain contaminated groundwater within a capture zone. IFPA is then applied to maximize the total annual pumping from existing wells in Rhis-Nekor unconfined coastal aquifer on the northern of Morocco. The obtained results indicate that IFPA is a promising method for solving groundwater management problems as it outperforms the standard FPA and other algorithms applied to the case studies considered, both in terms of convergence rate and solution quality.
... The Genetic Algorithm (GA) [17] is a population-based optimization method, proposed in the 60's, extended and popularized in 1989 [12], that belongs to the family of Evolutionary Algorithms [44]. In 1989 extended and made popular, in the context of optimization [12]. ...
Stereo correspondence is a well-established research topic and has spawned categories of algorithms combining several processing steps and strategies. One core part to stereo correspondence is to determine matching cost between the two images, or patches from the two images. Over the years several different cost metrics have been proposed, one being the Census Transform (CT). The CT is well proven for its robust matching, especially along object boundaries, with respect to outliers and radiometric differences. The CT also comes at a low computational cost and is suitable for hardware implementation. Two key developments to the CT are non-centric and sparse comparison schemas, to increase matching performance and/or save computational resources. Recent CT algorithms share both traits but are handcrafted, bounded with respect to symmetry, edge lengths and defined for a specific window size. To overcome this, a Genetic Algorithm (GA) was applied to the CT, proposing the Genetic Algorithm Census Transform (GACT), to automatically derive comparison schemas from example data. In this paper, FPGA-based hardware acceleration of GACT, has enabled evaluation of census windows of different size and shape, by significantly reducing processing time associated with training. The experiments show that lateral GACT windows produce better matching accuracy and require less resources when compared to square windows.
... Genetic algorithms are part of evolutionary algorithms inspired by the theory of evolution [46][47][48]. ...
The objective of this work is to develop a methodology for the automatic generation of optimised and innovative machining process planning that enable aeronautical subcontractors to face current productivity and competitiveness issues. A four-step methodology is proposed, allowing the user to obtain optimised machining ranges that respect his know-how and experience and introduce innovation. This methodology is based on a representation of the decisional behaviour of the user in a given situation as well as in the face of the risk of industrialisation and broadens the formalisation of the performance of a process by taking into account other performance criteria other than machining time or overall cost. A genetic algorithm is used to generate optimized process planning. An AHP method is used to represent the decision-making process. The methodology presents the best processes generated and the use of social choice theory enables it to target the most efficient ranges to be implemented, by integrating a risk criterion to the industrialization.
... In particular, the genetic algorithm is appropriate for solving significantly difficult mathematical problems with many variables and constraints because it shows excellent search performance in a complex solution space [4]. Another advantage of the genetic algorithm is that the high flexibility of the model application makes it easy to add constraints and an objective function [8,[21][22][23]. ...
In this study, a genetic algorithm was used to calculate the scheduled waiting time according to the train operation frequency of heterogeneous trains operating on one track. The acquired data were then used to determine the appropriate subsidiary track at which high-speed trains can load or release cargo away from low-speed trains. A metaheuristic genetic algorithm was applied and implemented using Javascript/jQuery. Six cases were investigated, which provided values of subsidiary track that vary according to the operation frequencies of different types of trains, and solutions were derived through 100 simulations using a stochastic method. The analysis results showed that the train overtaking frequency was the highest at the third intermediate station within the simulation, suggesting that this particular station requires a subsidiary track, even if the operating frequency of each train differs across the entire track considered in this study. The results of this study are expected to facilitate objective and practical planning during railway construction.
... In the late 1960s, professor Holland and his colleagues first proposed. In the early 1970s, a relatively complete bionic algorithm was formed [7] . In the 1970s, De Jong used genetic algorithm to carry out a large number of functional optimization [3] . ...
With its strong search ability and other characteristics, the genetic algorithm in mobile robot path optimization has been rapidly developed and applied, and has become one of the most successful algorithms to solve the mobile robot path planning problem. Firstly, this paper introduces the basic principle of genetic algorithm and gives a brief overview of its development and improvement strategies. Then, combining several representative improvement strategies, path planning of mobile robot is explained in detail. Finally, combining the theoretical and applied research results of genetic algorithm, the further development of the algorithm is summarized and prospected.
... Multimodal optimization can be tackled in a number of ways [7]. A wide research eld in multimodal optimization consists in niching methods [12,13], clearing [16], sharing [9,15]. As opposed to restart algorithms, these methods preserve diversity during the optimization run. ...
We study a test-based population size adaptation (TBPSA) method, inspired from population control, in the noise-free multimodal case. In the noisy setting, TBPSA usually recommends, at the end of the run, the center of the Gaussian as an approximation of the optimum. We show that combined with a more naive recommendation, namely recommending the visited point which had the best fitness value so far, TBPSA is also powerful in the noise-free multimodal context. We demonstrate this experimentally and explore this mechanism theoretically: we prove that TBPSA is able to escape plateaus with probability one in spite of the fact that it can converge to local minima. This leads to an algorithm effective in the multimodal setting without resorting to a random restart from scratch.
... Among nature-inspired algorithms, the genetic algorithm (GA) was probably the most well-known example. GA was developed by John Holland [16], which is an evolutionary algorithm. ...
This book discusses all the major nature-inspired algorithms with a focus on their application in the context of solving navigation and routing problems. It also reviews the approximation methods and recent nature-inspired approaches for practical navigation, and compares these methods with traditional algorithms to validate the approach for the case studies discussed. Further, it examines the design of alternative solutions using nature-inspired techniques, and explores the challenges of navigation and routing problems and nature-inspired metaheuristic approaches.
... In computer science, the Meta-heuristic optimization is the set of operations and technique models, use randomness to optimization the candidates and find the best solution [1]. Many Meta-heuristic optimization algorithms inspired by nature [2] some of them are, Particle Swarm Optimization (PSO) [3], Genetic Algorithm (GA) [4] Grey Wolf Optimizer Ant Colony Optimization (ACO) [5], Gravitational Search Algorithm (GSA) [6], Bat search Algorithm (BA) [7] and Dolphin Echolocation [8]. The flexibility of deal with different problems and the high performance of these algorithms make them more popular than tradition optimization technique. ...
... Evaluation of the performance of feature extraction methods are presented as well for demonstrating the learning strategies in applications. Holland (1975) rst developed GA, which has long been considered the basis of evolutionary computing (Figures 7.3 and 7.4). The GA selects parent 1 and parent 2 in the population pool, both of which mimic the gene structure in a chromosome for evolution numerically. ...
... Genetic algorithm (GA) is an evolutionary search technique ba-sed on the fact of natural genetics. It is a random search technique, developed by Holland in 1960 [14] and later it was popularized by Goldberg. One of the significant parameters in the GA is chromosome. ...
Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.
Предложен алгоритм имитации отжига для построения многопроцессорных списочных расписаний минимальной длительности с дополнительным ограничением на количество передач между процессорами. Данное ограничение характерно для вычислительных систем с жесткими ограничениями на ресурсы межпроцессорной сети передачи данных. В целом задача минимизации длительности расписания возникает при разработке систем обработки данных в реальном масштабе времени, таких как бортовые и телекоммуникационные системы. Также задача актуальна для периферийных вычислений (edge computing). Экспериментальное исследование свойств алгоритма показало его высокую точность, стабильность и масштабируемость.
Reducing the cost of concrete construction as the most expensive building material reduces the overall cost of construction high-strength concrete (HSC). In the present study, to achieve this goal, the mix design of HSC is optimized in terms of strength and price using the meta-heuristic genetic algorithm (HGA). To do this, in the first step, a series of experimental data was considered as basic information and then, a mix design function was obtained to determine 28-day compressive strength and a slump calculation function using meta-HGA m in MATLAB software. In the next step, strength-price function was optimized using the meta HGA by changing the material ratios in the mix design, the price of the materials in each mix design and applying the required conditions of HSC including slump obtained from slump calculation function. Then, a comparison was performed between the results of this algorithm and the regression method, the results showed the better responses of the algorithm compared to regression for both factors of strength and price with the values of 10.2% and 6.5%, respectively. In addition, the construction of the mix design resulting from responses of the algorithm and regression in laboratory indicates that more than 97% of the strength was achieved at 28 days of age.
This invention is a niching optimization algorithm that
provides multiple extrapolated points of a function, the
maximum or minimum outputs, over one optimization run.
Yet differently than existing niching algorithms, it locally
ranks each local population, providing a multi - focus explo
ration with an equalized number of solutions inside each
niche, and identifies the set of most efficient and distinct
solutions, instead of the overall population of solution. As an
optimization algorithm, it has broad application in artificial
intelligence, and in the design of engineering systems such
as aeronautical structures, etc ... It also can generate a
mesh for any dataset or function domain, grouping inputs by
regions of similitude. Thus, it can generate a mesh for a
FEM, and segment the domain of expensive metamodels,
like artificial neural networks, Kriging models ( KR ), among
others. Experiments demonstrated that with a response surface mesh, the overall training time is substantially reduced.
Abstract
In today's marketplace, companies are allocating too much budget to distribution systems in order to
supply with rapidly increasing competitive environment and customer demands quickly and effectively. For this
reason, companies attach much more importance to the transport of products, services and information so that
minimum cost, minimum time and optimum route can be obtained. The addressed problem is the problem of a
vehicle returning to the starting point by going through all the streets in a neighborhood. In the literature, it is
called the Chinese Postman Problem (CPP). The problem modeled as CPP in the study was solved by the
Genetic Algorithm (GA) and Geographic Information System (GIS) software, which is solved with the plugin
QGIS program. Optimal routes were obtained in both applications and the results were compared. It is aimed at
minimizing the time and cost by obtaining the optimal route.
Keywords: Arc Routing, Chinese Postman Problem, Genetic Algorithm, Geographic Information System (GIS)
Öz
Günümüz piyasasında, hızla artan rekabet ortamı ve müşteri taleplerini hızlı ve etkin bir şekilde
karşılamak için firmalar, dağıtım sistemlerine çok fazla bütçe ayırmaktadırlar. Bu nedenle firmalar ürün, hizmet
ve bilgilerin minimum maliyet, minumum süre ve optimum rota elde edilecek şekilde taşınmasına çok daha fazla
önem vermektedirler. Ele alınan problem, bir aracın bir mahalle içerisindeki bütün caddelere uğrayarak başladığı
noktaya geri dönmesi problemidir. Literatürde Çinli Postacı Problemi (ÇPP) olarak adlandırılmaktadır. Bu
çalışmada ÇPP olarak modellenen problem, Genetik Algoritma (GA) ve Coğrafi Bilgi Sistemi (CBS) yazılımı
olan QGIS programının eklentisi ile çözülmüştür. Her iki uygulama da optimal rotalar elde edilerek çıkan
sonuçlar karşılaştırılmıştır. Optimal rotanın elde edilmesi ile zamanın ve maliyetin de minimizasyonu
amaçlanmıştır.
Anahtar Kelimeler: Ayrıt Rotalama, Çinli Postacı Problemi, Genetik Algoritma, Coğrafi Bilgi Sistemi
(CBS).
Warpage deformation is a common defect in the glass fiber-reinforced plastic (GFRP) injection molding process. In this study, a case study of the GFRP product is proposed. Firstly, with the minimum warpage deformation as the optimization objective, three significant factors affecting product quality (the first stage packing pressure, injection time, and melt temperature) are selected from nine process parameters by using Plackett Burman. Then, the three selected significant factors are considered as design variables, and Box-Behnken design (BBD) is used to design the experiment. Warpage analysis is performed based on Moldflow simulation software. Secondly, on the basis of the BBD experimental samples and results, a regression model by compound optimization, back-propagation neural network based on adaptive boosting and genetic algorithm (AdaBoost-GA-BP) model is established. To further illustrate the prediction accuracy of the model, response surface methodology model, back propagation neural network (BPNN) model, back-propagation neural network based on genetic algorithm (GA-BPNN) model are taken as comparative algorithms. The results show that the prediction system of the AdaBoost-GA-BP model has good stability and accuracy. Finally, the particle swarm optimization approach is used to search for the minimum warpage. It can be concluded that the quality of the plastic part after optimization has been improved.
In the previous few decades, all companies have produced data in large amounts from different sources. It can be from business applications of their own, social media or other web outlets, from smartphones, and client computing devices or from the Internet of Things sensors and software. This knowledge is highly useful for companies that have resources in place to build on it. The overall toolbox for these methods is called data analytics.
Data analytics is used to represent those methods that provide an essential arrangement of the data. It can be classified into four categories, including descriptive, predictive, diagnostic, and prescriptive data analytics. Out of these methods, predictive analytics is the most dynamic approach for data analytics that involves an advanced statistical approach, Artificial Intelligence–based algorithms. Predictive analytics (PA) is the member of advanced analytics that is broadly utilized in the prediction of uncertain future events. A variety of data analysis, statistical modeling, and theoretical approaches are used to bring management, information technology, and business process forecasting together to forecast these predictive events. To define threats and possibilities in the future, the trends contained in historical and transactional data may be used. PA models may track relationships with a complex set of conditions to distribute a score or weighting among several variables to determine risk.
To strengthen management of supply chain and increase its efficiency, how to step up the research on supply chain distribution network system has become a major topic on logistics. As traditional logistics can no longer suit the requirements for great circulation due to a waste of resources, it is necessary for us to establish distribution systems with network structure, which are composed of some nodes and lines, whose activities serve as a foundation of supply chain distribution system activity. Supply chains cover everything from production, to product development, to the information systems needed to direct these undertakings. One of the important loops supply chain is distribution network. Design and analysis of the distribution network is one of the most important issues facing the distribution companies. The importance of this issue from the fact that logistics costs is in the main line items cost of sales and distribution companies. In recent years, the main problem in the design of the distribution network, minimizing of all the costs associated with it which includes the cost of production, transportation, storage of inventory, and too locating of distribution centers and routing sending products between the loops of supply chain. This study develops a mixed integer nonlinear
programming (MINLP) model to design a supply chain. In this paper a bi-objective model for a distribution network in a three-echelon supply chain of multi-product multi-period is considered. The network is consist of manufacturing plants, distribution centers and customer. In this model, some factors including of climate situation on distribution network in form of the concept of reliability is investigated. In addition, in this study, with considering some costs as fuzzy and studying the effect of inflation on costs of distribution centers, is tried the proposed model is closer to real-world problems. Since this model has high complexity, so for solving this model, is used a meta-heuristic method such as Genetic algorithm. Finally the obtained results is analyzed.
As the cutting stone is a wear process, performing this process with diamond pieces' aid can be considered the wear of stone particles bypassing diamond grains on its surface. To better understand this process as well as the conditions governing the cutting diamond grain, it is necessary to familiar with the cutting mechanism along with the affecting parameters. In this matter, predicting the amount of segment consumption plays a prominent role to estimate the production cost as well as to schematize the building stone mines. This paper utilized the data obtained from Carbonate and Granite stones to estimate the amount of consumption of diamond cutting wire segments. To do so, two methods, namely support vector regression (SVR) and genetic algorithm + Multilayer perceptron (GA-MLP) were chosen using the MATLAB software toolboxes in order to estimate the segment erosion. In each of the above algorithms, a lowpass smoothing filter, called Savitzky-Golay was employed on the data. For this purpose, three rock properties including uniaxial compressive strength, Shimazk friction factor, and Young's modulus, were also employed as the model's input. After that, twelve models were constructed and then the segment erosion was estimated as well. Ultimately, the accuracy of the above models was assessed using the coefficient of determination (), mean square error (MSE), root mean square error (RMSE), mean value absolute error (MAD), mean absolute percentage error (MAPE), and variance of factor analysis (VFA). According to the obtained results, it can be concluded that the SVR approach and the Savitzky-Golay filter with Polynomial Kernel could better estimate the wear rate of the diamond cutting wire segment.
In this manuscript we propose a novel method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.
Floods are a climatic phenomena that affect different regions worldwide and that produces both human and material losses; for example in 2017, six of the worst floods were the cause of 3.273 deaths worldwide. In Colombia, the strong winter wave presented between 2010 and 2011, caused 1,374 deaths and 1,016 missing persons. The main river in Colombia is the Magdalena, which provides great benefits to the country but is also susceptible to flooding. This article presents a proposal to optimize a fuzzy system to prevent flooding in homes adjacent to areas of risk to the Magdalena River. The method used is based on evolutionary algorithms to perform a global search, including a gradient-based algorithm to improve the solution obtained. The best result achieved was the Mean Square Error (MSE) of 7, 83E - 05. As a conclusion, it is needed to employ optimization methods for the adjustment of parameters of the fuzzy system when considering that the sets and the rules are systematically obtained.
Le premier chapitre présente les grandes lignes des algorithmes génétiques avec l'accent missur les différents opérateurs utilisés et les paramètres à régler pour une bonne mise enoeuvre. Les avantages et les inconvénients de cette méthode seront discutés ainsi que lesmultiples possibilités qu'offre cette approche.Le chapitre deux traite du principal problème sur lequel les algorithmes génétiques vont^etre appliqués. Il s'agit d'optimiser la localisation d'antennes ainsi que leur nombrepour maximiser la performance d'un interféromètre lors d'une utilisation dans un environnement électromagnétique sévère. Après avoir présenté les équations à résoudre etramené le problème sous forme d'un problème d'optimisation, les di érents paramètresintervenant seront discutés afin de mettre à jour leur signification physique.Le chapitre trois présente un certain nombre de résultats numériques dans di érentes con-gurations possibles du problème. L'application des algorithmes génétiques pour obtenirces solutions mènera à une série de discussions sur le choix de la méthode.Le chapitre quatre marque un retour vers le plus simple des interféromètres, celui constituéde seulement trois antennes alignées. Il est alors montré qu'il est possible de déterminerles différents optima globaux de la fonctionnelle à partir de la résolution d'expressions trèssimples. Avant de conclure, le chapitre cinq présentera une généralisation des différentsproblèmes abordés dans le cas d'un interféromètre plan pouvant plus aisément s'adapteraux supports présents dans la réalité.
Cette thèse est consacrée à l'étude des problèmes inverses, c'est-à-dire à l'identi cation de fonc-tions qui participent à un processus dont on connaît uniquement l'état initial et l'état final. Engénéral, il est possible d'avoir certaines informations sur cette fonction, mais elles ne sont passuffisantes pour utiliser des moyens d'approximation classiques.Les problèmes que nous allons étudier sont d'une part l'identification de la fonction isothermeen chromatographie et d'autre part un problème de robotique mobile, qui consiste à trouverune trajectoire réalisable par un véhicule pour se garer, à partir d'un point quelconque, vers unautre point.Pour ces deux applications, nous utiliserons les algorithmes d'évolution qui sont des algorithmesd'optimisations stochastiques d'ordre 0 inspirés de l'évolution darwinienne. En d'autres termes,ce sont le ou les structures les mieux adaptées à un certain environnement qui survivront etréussiront à se reproduire. L'évolution a lieu à travers une succession de générations pendantlesquelles on fait évoluer une population de points de l'espace de recherche (individus), touten appliquant des opérateurs de sélection, de mutation et de croisement afin de donner naissance à des individus toujours plus performants. Le but de ces algorithmes est de minimiser(ou maximiser) la fonction de performance ou fonction defitness, fonction qui va de l'espace derecherche vers IR et qui traduit la perfomance de chaque individu constituant la population. Parrapport aux méthodes déterministes qui sont basées sur l'existence de dérivées, les algorithmesd'évolution ne demandent que la connaissance de la valeur de la fitness de chaque individu dela population. De plus, ils permettent de trouver un optimum global, ce qui n'est pas le casdes méthodes classiques dont le résultat est lié au choix des conditions initiales et qui donc nes'appliquent que localement.
This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results.
This study aimed to develop pH sensitive polymethacrylic acid-chitosan-polyethylene glycol (PCP) nanoparticles for oral insulin delivery. This was achieved by dispersion polymerization of methacrylic acid (MAA), polyethylene glycol (PEG) and chitosan (CS) in the presence of a cross linking agent, ethylene glycoldimethacrylate (EGDMA), and a polymer initiator, potassium per sulphate. Method development was carried out based on fractional factorial design by varying process parameters such as ratio of MAA to CS, ratio of MAA to EGDMA and the initial amount of insulin used to prepare PCP nanoparticles. PCP nanoparticles were characterized with different techniques including FTIR, DLS, and scanning electron microscopy (SEM). Insulin was incorporated into the nanoparticles by a diffusion filling method. It was found that the PCP nanoparticles exhibited good protein encapsulation efficiency (up to 99.9%). The findings revealed that the nanoparticles were spherical with smooth surfaces. The particle size average was determined to be 172 nm by DLS and 86 nm by SEM. The in vitro release profiles of PCP nanoparticles were investigated both in acidic (simulated gastric fluids, pH: 1.2) and neutral buffered solutions (simulated intestinal fluids, pH: 7.4). In order to have the best performance of nanoparticles, the process parameters were optimized using a support vector regression (SVR) method in combination with genetic algorithms (GA). The results revealed that the optimum settings were as follows: MAA/CS mole ratio (%): 297.35, CS/EGDMA mole ratio (%): 51.4, and the initial insulin amount (mg): 50.3. The findings showed that nanoparticles exhibited a pH responsive release profile where the extent of drug release in simulated intestinal medium was almost two fold more than the simulated gastric media. Global sensitivity analysis was also used to identify the impact of different variables on the PCP nanoparticle characteristics. This study introduces a new approach to rational design of nanoparticles according to the properties of interest.
طراحی و تحلیل شبکهی توزیع در یک زنجیرهی تأمین یکی از مهمترین مسائل پیش روی شرکتهای توزیعکننده است. در این مقاله به بهینهسازی دوهدفهی شبکهی توزیع در یک زنجیرهی تأمین سهسطحی در حالت چندمحصولی - چند دورهیی که شامل کارخانجات تولیدی و مراکز توزیع و مشتریان است، پرداخته شده است. در تحقیق پیش رو سعی شده است اثر برخی از عوامل مانند شرایط آب و هوایی، حملات تروریستی و ... بر روی شبکههای توزیع در قالب مفهوم قابلیت اطمینان بررسی شود. در این مقاله همچنین سعی شده است با فازی در نظر گرفتن برخی از هزینهها و بررسی اثر تورم بر روی هزینههایی که مراکز توزیع با آنها درگیرند هرچه بیشتر مسئله به مسائل مرتبط در دنیای واقعی نزدیکتر شود. بنابراین افزودن قابلیت اطمینان بهعنوان یک هدف جدید و همچنین بررسی اثر تورم بر روی هزینههای مراکز توزیع از جمله جنبههای نوآوری در این تحقیق هستند. از آنجا که این مسئله پیچیدگی بالایی دارد و جزء مسائل NP-hard است، از روش فراابتکاری ژنتیک در حل آن استفاده شده و در نهایت نتایج به دست آمده تحلیل و بررسی شده است.
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