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Publications
Publications (125)
Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of adequate CMOPs for benchmarking is difficult and lacks a formal background. This paper takes a step towards addressing this issue...
Constraint handling in multiobjective optimization is more complex than in single-objective optimization, where the values of the objective and constraints are easier to combine. To gain insight into the characteristics of constraint handling techniques (CHTs) for multiobjective optimization, we explore their effect independently from search method...
Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of adequate CMOPs for benchmarking is difficult and lacks a formal background. This paper addresses this issue by exploring CMOPs fr...
Despite the increasing interest in constrained multiobjective optimization in recent years, constrained multiobjective optimization problems (CMOPs) are still insufficiently understood and characterized. For this reason, the selection of appropriate CMOPs for benchmarking is difficult and lacks a formal background. We address this issue by extendin...
Modern elevator systems are controlled by the elevator group controllers that assign moving and stopping policies to the elevator cars. Designing an adequate elevator group control (EGC) policy is challenging for a number of reasons, one of them being conflicting optimization objectives. We address this task by formulating a corresponding constrain...
Despite the increasing interest in constrained multiobjective optimization in recent years, constrained multiobjective optimization problems (CMOPs) are still unsatisfactory understood and characterized. For this reason, the selection of appropriate CMOPs for benchmarking is difficult and lacks a formal background. We address this issue by extendin...
Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely applied, it has several drawbacks, such as limited measurem...
Context recognition (CR) systems infer the user’s context, such as their physical activity, from sensor data obtained, for example, with smartphone sensors. Designing an energy-efficient CR system, however, is a complex optimization problem involving conflicting objectives and several constraints arising from real-world limitations and designers’ p...
This book constitutes the refereed proceedings of the 9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020, held in Brussels, Belgium, in November 2020.
The 24 full papers presented in this book were carefully reviewed and selected from 68 submissions.
The papers in this BIOMA proceedings specialized i...
Home energy-management systems can optimize performance either by computing the next step dynamically – online, or rely on a precomputed strategy used to introduce the next decision – offline. Further, such systems can optimize based on only one or several objectives. In this paper, the multi-objective optimization of offline strategies for home en...
This work optimizes the thrusting profile of a low-thrust spacecraft propelled by an ion engine to raise from Earth’s low orbit to the vicinity of the Moon. The orbital raising phase is divided uniformly into sixteen sections, of which the first six are set to full propagation to escape early from the radiation belts, and the profiles of the other...
Many real-world optimization problems involve both multiple objectives and constraints. Although constraint handling in multiobjective optimization has been considered in the literature, there is still a high demand for more advanced and versatile constraint handling techniques (CHTs) in real-world applications. For this reason, we propose a genera...
Finding a suitable control strategy for the elevator group controller (EGC) is a complex optimization problem with several objectives. We utilize the sequential-ring (S-Ring) model of EGC systems and propose a biobjective formulation of the EGC optimization problem. Unlike the previous work, we use true multiobjective optimizers in solving this pro...
We revisit our previous work on comparing black-box differential evolution (BBDE) and classic differential evolution, where we observed an unexpected residual sensitivity to rotation of BBDE. A detailed analysis of the results shows that it stems from a constraint handling technique that favours separability. Equipped with these findings, we redesi...
Recently, black-box differential evolution (BBDE) has been proposed to overcome the search biases and sensitivity to rotation of the classic differential evolution (DE). To date, BBDE has been studied only for the 'rand' strategy and even for this strategy, no systematic experimental study has been published yet. In this paper we provide such a stu...
In multiobjective optimization, many techniques are used to visualize the results, ranging from traditional general-purpose data visualization techniques to approaches tailored to the specificities of multiobjective optimization. The number of specialized approaches rapidly grows in the recent years. To assist both the users and developers in this...
Human driving models aim at producing human-like driving strategies by mimicking the behavior of drivers. Drivers optimize several objectives when traveling along a route, such as the traveling time and the fuel consumption. However, these objectives are not taken into account when building human driving models. To overcome this shortcoming, we des...
Quality control of the commutator manufacturing process can be automated by means of a machine learning model that can predict the quality of commutators as they are being manufactured. Such a model can be constructed by combining machine vision, machine learning and evolutionary optimization techniques. In this procedure, optimization is used to m...
Meta-heuristic algorithms should be compared using the best parameter values for all the involved algorithms. However, this is often unrealised despite the existence of several parameter tuning approaches. In order to further popularise tuning, this paper introduces a new tuning method CRS-Tuning that is based on meta-evolution and our novel method...
The paper presents the performance of the DEMO (Differential Evolution for Multiobjective Optimization) algorithm on the new bbob-biobj suite of test problems. After limited parameter tuning that comprised different environmental selection procedures, population sizes and crossover probabilities, we identify a parameter setting different from the d...
This paper proposes a novel surrogate-model-based multiobjective evolutionary algorithm called Differential Evolution for Multiobjective Optimization based on Gaussian Process models (GP-DEMO). The algorithm is based on the newly defined relations for comparing solutions under uncertainty. These relations minimize the possibility of wrongly perform...
Prispevek opisuje programsko opremo za pametno in celovito vodenje sistemov v stavbi, kot so ogrevanje, prezračevanje, senčenje, razsvetljava in upravljanje z viri energije. Cilj je zagotoviti čim nižje stroške in hkrati čim višje udobje za stanovalce. Sistem pametne stavbe pridobi podatke s senzorjev, nameščenih v stavbi, in se iz njih nauči navad...
Most real-world engineering optimization problems are inherently multiobjective, for example, searching for trade-off solutions of high quality and low cost. As no single optimal solution exists for such problems, multiobjective optimizers provide sets of optimal (or near-optimal) trade-off solutions to choose from. The empirical attainment functio...
In multiobjective optimization, the Empirical Attainment Function (EAF) can be used to determine which areas of the objective space are attained by an optimization algorithm. If two algorithms are to be compared, differences in EAF values show which areas of the objective space are more often attained by one of the algorithms. While the visualizati...
Due to various reasons the solutions in real-world optimization problems cannot always be exactly evaluated but are sometimes represented with approximated values and confidence intervals. In order to address this issue, the comparison of solutions has to be done differently than for exactly evaluated solutions. In this paper, we define new relatio...
In evolutionary multiobjective optimization, it is very important to be able to visualize approximations of the Pareto front (called approximation sets) that are found by multiobjective evolutionary algorithms. While scatter plots can be used for visualizing 2-D and 3-D approximation sets, more advanced approaches are needed to handle four or more...
This book constitutes the refereed proceedings of the 13th International Conference on Parallel Problem Solving from Nature, PPSN 2013, held in Ljubljana, Slovenia, in September 2014.
The total of 90 revised full papers were carefully reviewed and selected from 217 submissions. The meeting began with 7 workshops which offered an ideal opportunity t...
The problem of optimal resource allocation in spatially distributed networks appears, for example, in wireless telecommunications and consists of maximising the network utility, i.e., the fee paid by the users, and minimising the costs of installing the allocated resources. This bi-objective optimisation problem can be approached in several ways, a...
When analyzing the performance of a bi-objective optimization algorithm, the empirical attainment function (EAF) is often used to visualize the attained parts of the objective space. Similarly, when comparing two algorithms, the differences in EAF values can be used to show the parts of the objective space in which the first algorithm outperforms t...
This presentation starts with two case studies of applying optimization methodology in industry, one involving numerical optimization based on simulation models, and the other combinatorial optimization with specific constraints and objectives. These case studies serve to identify some of the challenges frequently met by solution providers for indu...
When a person drives a vehicle along a route, he/she optimizes two objectives, the traveling time and the fuel consumption. Therefore, the task of driving can be viewed as a multiobjective optimization problem and solved with appropriate optimization algorithms. The comparison between the driving strategies obtained by humans and those obtained by...
This paper proposes a hybrid self-adaptive evolutionary algorithm for graph
coloring that is hybridized with the following novel elements: heuristic
genotype-phenotype mapping, a swap local search heuristic, and a neutral
survivor selection operator. This algorithm was compared with the evolutionary
algorithm with the SAW method of Eiben et al., th...
Continuous casting is a widely used steel production process. To yield high-quality steel, the casting parameters have to be tuned with respect to several contradictory criteria. We approached this multiobjective optimization problem in discrete and continuous variants, applying Exhaustive Search (ES) and Differential Evolution for Multiobjective O...
To address the needs of rapidly changing energy markets, an energy data management system capable of supporting higher utilization of renewable energy sources is being developed. The system receives flexible offers from producers and consumers of energy, aggregates them on a regional level and schedules the aggregated flexible offers to balance for...
The purpose of alternative energy supply systems is to produce electrical energy at the location of its consumption, independently from the supply grid, and exploiting renewable energy sources, such as sunlight and wind. Rapidly changing conditions on energy markets and strengthening environmental requirements make alternative energy supply systems...
Nowadays, Renewable Energy Sources (RES) are attracting more and more interest. Thus, many countries aim to increase the share of green energy and have to face with several challenges (e.g., balancing, storage, pricing). In this paper, we address the balancing challenge and present the MIRABEL project which aims to prototype an Energy Data Manageme...
Abstract In this paper, we present AMS-DEMO, an asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multi-objective optimization. AMS-DEMO was designed for solving time-intensive problems efficiently on both homogeneous and heterogeneous parallel computer architectures. The algorithm is used as a test case for the asynch...
Driving a vehicle along a route consists of control actions applied to the vehicle by taking into account the vehicle and route states. Control actions are usually selected by optimizing the traveling time and the fuel consumption. However, the resulting vehicle behavior can be uncomfortable for the driver/passengers. The comfort is measured as the...
Synthesis of cellular automata is an important area of modeling and describing complex systems. Large amounts of combinations and candidate solutions render the usage of deterministic approaches impractical and thus nondeterministic optimization methods have to be employed. Two of the typical evolutionary approaches to synthesizing cellular automat...
This paper presents a deterministic multiobjective optimization algorithm for discovering driving strategies. The goal is to find a set of nondominated driving strategies with respect to two conflicting objectives: time and fuel consumption. The presented multiobjective algorithm is based on the breadth-first search algorithm and Nondominated Sorti...
In ideal multiobjective optimization, the result produced by an optimizer is a set of nondominated solutions approximating the Pareto optimal front. Visualization of this approximation set can help assess its quality as well as present various features of the problem. Most often, scatter plots are used to visualize 2D and 3D approximation sets, whi...
Gaussian process (GP) models are non-parametric, black-box models that represent a new method for system identification. The optimization of GP models, due to their probabilistic nature, is based on maximization of the probability of the model. This probability can be calculated by the marginal likelihood. Commonly used approaches for maximizing th...
Tuning parameters of an evolutionary algorithm is the essential phase of a problem solving process since the parameter values significantly influence the algorithm efficiency. A traditional parameter tuning approach finds a setting of parameter values that is then used for solving various problem instances. Clearly, such parameter values may not pe...
The task of marker optimization in clothing production is to eliminate pieces from a work order using an optimal sequence of markers and plies, where the work order is given as a matrix of colors by sizes, markers are vectors of sizes to be laid-out and cut together, and the number of plies determines how many pieces are eliminated from the work or...
Ant-Colony Optimization (ACO) is a popular swarm intelligence scheme known for its efficiency in solving combinatorial optimization problems. However, despite some extensions of this approach to continuous optimization, high-dimensional problems remain a challenge for ACO. This paper presents an ACO-based algorithm for numerical optimization capabl...
Evolutionary computation is an area of computer science utilizing the mechanisms of biological evolution in computer problem solving. It is concerned with theoretical studies, design and application of stochastic optimization procedures, known as Evolutionary Algorithms (EAs). EAs have proven effective and robust in solving demanding optimization p...
This paper investigates the problem of optimal assignment of wireless interfaces for service delivery in the context of a heterogeneous wireless access system. We formulate service requests initiated by the applications running on the mobile device as a multiobjective optimization (MOO) problem: optimal mapping of application requirements to wirele...
The optimization of markers is one of the most important preparatory steps for production in the clothing industry. It determines the sizes of clothes to be laid and cut together. Markers are built on a work order basis. The work order is a matrix of sizes by colors that have to be covered by markers in an optimal way. The optimal way can be define...
In electrical discharge machining (EDM), appropriate average current in the gap has to be selected for the given machining
surface in order to obtain the highest material removal rate at low electrode wear. Thus, rough machining parameters have
to be selected according to the machining surface. In the case of sculptured features, the machining surf...
The efficiency of universal electric motors that are widely used in home appliances can be improved by optimizing the geometry
of the rotor and the stator. Expert designers traditionally approach this task by iteratively evaluating candidate designs
and improving them according to their experience. However, the existence of reliable numerical simul...
This paper reports on preliminary numerical experiments in optimizing coolant flows in continuous casting of steel with respect to multiple objectives. For this purpose, Differential Evolution for Multiobjective Optimization (DEMO) coupled with a reliable numerical simulator of the casting process was applied. The algorithm parameters were initiall...
This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2
and IBEA and their differential evolution based variants DEMO
\textNS-II^\text{NS-II}
, DEMO
\textSP2^\text{SP2}
and DEMO
\textIB^\text{IB}
. Experimental results on 16 numerical multiobjective test problems show that on t...
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task calls for fast, incremental and robust learning algori...
Genetic algorithm is an evolutionary search technique that is becoming increasingly popular in solving practical problems like timetabling, scheduling, engineering design, and other optimization problems. In this paper we present a computer program implemented to perform basic experimentation with a simple genetic algorithm with intention to gain u...
Optimizing combinations of placements of parts, known as markers, is an important preparatory step in order-based industrial production of clothes. Given a work order in the form of a matrix of pieces in size numbers and designs, the task is to find a list of combinations of size numbers to com- plete the work order. The outcome of this step influe...
This paper examines several different approaches to exploiting structural information in semi-structured document categorization. The methods under consideration are designed for categorization of documents consisting of a collection of fields, or arbitrary tree-structured documents that can be adequately modeled with such a flat structure. The app...
The paper presents a new distributed metaheuristic algorithm in an optimal control problem related to the cooling process in the continuous casting of steel. The optimization task is to tune 18 coolant flows in the caster secondary cooling system to achieve the target surface temperatures along the slab. Sequential search algorithms are proved inef...
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task calls for fast, incremental and robust learn-ing algor...
Abstract This paper summarizes our participation in the TREC 2006 spam track. We sub- mitted a single filter for the evaluation, based on the Prediction by Partial Matching compression scheme, a method that performed well in the previous TREC evalua- tion. A major focus of our effort was to improve efficiency of the method, particu- larly in terms...
We present an empirical study of process parameter tuning in industrial continuous casting of steel where the goal is to assure the highest possible quality of the cast steel through proper parameter setting. The process is assumed to be under steady-state conditions and the considered optimization task is to set 18 coolant flows in the caster seco...
Complexity of biological systems is one of the toughest problems for any experimental technique. Complex biochemical composition and a variety of biophysical interactions governing the evolution of a state of a biological system imply that the experimental response of the system would be superimposed of many different responses. To obtain a reliabl...
The paper deals with the possibilities of evaluating and increasing the stability of the deep drawing process. Not only the size of technological windows but also the stability of production processes is studied by means of numerical simulation based on the finite element method. An approach for optimization of the deep drawing processes is present...
Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications.
In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective
optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Pareto-based ranking...
Following the widely spread EPR spin-label applications for biosystem characterization, a novel approach is proposed for EPR-based characterization of biosystem complexity. Hereto a computational method based on a hybrid evolutionary optimization (HEO) is introduced. The enormous volume of information obtained from multiple HEO runs is reduced with...
This paper summarizes our participation in the TREC 2005 spam track, in which we consider the use of adaptive statistical data compression models for the spam filtering task. The nature of these models allows them to be employed as Bayesian text classifiers based on character sequences. We experimented with two dierent compression algorithms under...
This report examines several different approaches to exploiting structural informa-tion in semi-structured document classification. The methods range from trivial modi-fications of text modeling and classification algorithms to more elaborate classification schemes, specifically tailored to structured documents. We compare the performance of these...
We present an evolutionary algorithm approach to schedule optimization for a group of production lines in a car factory. Schedules are evaluated with respect to the energy consumption over peak demand periods, while the task is to minimize the energy costs by appropriately scheduling the interruptions of processes on the lines. Tests on real proble...
In continuous casting of steel a number of parameters have to be set, such as the casting temperature, casting speed and coolant flows that critically affect the safety, quality and productivity of steel production. We have implemented an optimization tool consisting in an optimization algorithm and casting process simulator. The paper describes th...
The popularity of search heuristics, has lead to numerous new approaches in the last two decades. Since algorithm performance is problem dependent and parameter sensitive, it is difficult to consider any single approach as of greatest utility over all problems. Despite this, differential evolution (DE) is a numerical optimization approach that requ...
Evolutionary computation can be of considerable use in interpreting and analyzing spectra of biological systems. This chapter focuses on the electron paramagnetic resonance (EPR) technology, and on the use of an evolutionary computational approach to aid the characterization of biological systems with EPR. EPR spectroscopy is a non-destructive insp...
Optimization of markers plays an important role in preparation of order-based industrial production of clothes. Given a matrix of pieces in size numbers and designs, the task is to find a list of combinations of size numbers to accomplish a work order. The outcome of this step influences the number of cut out pieces, the amount of material used in...
This report describes a greenhouse simulator. The described simulator is translated from a German description (Pohlheim and Heiner, 1996), and some minor modifications are introduced. The simulator is reimplemented in Java and is based on the original MatLab version. The purpose of the simulator is to explore various techniques for control of nonli...
Evolutionary algorithms for optimization of dynamic problems have recently received increasing attention. Online control is a particularly interesting class of dynamic problems, because of the interactions between the controller and the controlled system. In this paper, we report experimental results on two aspects of the direct control strategy in...
Evolutionary algorithms for optimization of dynamic problems have recently received increasing attention. Online control is a particularly interesting class of dynamic problems, because of the interactions between the controller and the controlled system. In this paper, we report experimental results on two aspects of the direct control strategy in...
We present an evolutionary computation approach to parameter tuning in electron paramagnetic resonance (EPR) spectroscopy which is a nondestructive technique suitable for inspection of complex biological systems. Characterization of such a system is much more reliable when spectral features are extracted from a biophysical model of the system. This...
The paper presents the design and evaluation of an adaptive signal processing procedure based on human skill. The focus is on interpreting probe signals detected in gas–liquid flow in the presence of noise where existing signal interpretation techniques may encounter difficulties. Interpretation of a probe signal requires construction of a correspo...
Electron paramagnetic resonance (EPR) spectroscopy is a nondestructive technique suitable for inspection of biological systems.
Characterization of such a system is much more reliable when relevant spectral characteristics are extracted from a biophysical
model of the system. To tune the model parameters, stochastic optimization techniques are used...
In spite of their practical success, knowledge-based systems still suffer from considerable limitations. Specialized for problem solving in a narrow domain, most systems possess very limited knowledge and are rather inflexible. Moreover, building a knowledge base is the most critical phase in developing an expert system. In overcoming these limitat...