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Number of papers with bio-inspired optimization and nature-inspired optimization in the title, abstract and/or keywords, over the period 2005–2019 (Scopus database)

Number of papers with bio-inspired optimization and nature-inspired optimization in the title, abstract and/or keywords, over the period 2005–2019 (Scopus database)

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In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. Thi...

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This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest. Each individual in the...
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span>In this work two ground-breaking algorithms called; Sperm Motility (SM) algorithm & Wolf Optimization (WO) algorithm is used for solving reactive power problem. In sperm motility approach spontaneous movement of the sperm is imitated & species chemo attractant, sperms are enthralled in the direction of the ovum. In wolf optimization algorithm...
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Despite steady advance in computing power, the number of function evaluations in global optimization problems is often limited due to time-consuming analyses. In structural optimization problems, for instance, these analyses are typically carried out using the Finite Elements Method (FEM). This issue is especially critical when dealing with bio-ins...

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... Among the stochastic global optimization techniques (also called metaheuristics) currently the most popular ones are evolutionary computation (EC) algorithms (also called nature-inspired or bio-inspired algorithms). These methods imitate biological processes such as natural selection, or evolution, where solutions are represented as individuals that reproduce and mutate to generate new, potentially improved candidate solutions for the given problem [27]. Other EC methods try to mimic the collective behavior of simple agents, giving rise to the concept of swarm intelligence [28]. ...
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The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select twelve well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated.
... Indeed, the proposal of new methods has become problematic, leading to a proliferation of techniques with similar operations but different names. This issue has been highlighted multiple times in recent years [21][22][23]. ...
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Quantum computing (QC) is expected to solve incredibly difficult problems, including finding optimal solutions to combinatorial optimization problems. However, to date, QC alone is still far to demonstrate this capability except on small-sized problems. Hybrid approaches where QC and classical computing work together have shown the most potential for solving real-world scale problems. This work aims to show that we can enhance a classical optimization algorithm with QC so that it can overcome this limitation. We present a new hybrid quantum-classical tabu search (HQTS) algorithm to solve the capacitated vehicle routing problem (CVRP). Based on our prior work, HQTS leverages QC for routing within a classical tabu search framework. The quantum component formulates the traveling salesman problem (TSP) for each route as a QUBO, solved using D-Wave's Advantage system. Experiments investigate the impact of quantum routing frequency and starting solution methods. While different starting solution methods, including quantum-based and classical heuristics methods, it shows minimal overall impact. HQTS achieved optimal or near-optimal solutions for several CVRP problems, outperforming other hybrid CVRP algorithms and significantly reducing the optimality gap compared to preliminary research. The experimental results demonstrate that more frequent quantum routing improves solution quality and runtime. The findings highlight the potential of integrating QC within meta-heuristic frameworks for complex optimization in vehicle routing problems.
... While existing comparative studies, like the study of Khalife et al. in a previous work (Khalife et al. 2022), there has been a lack of direct evaluation studies specifically focusing on the application of these algorithms to clothoid-based AGV trajectory optimization. This paper fills this gap with a comprehensive comparative study of bio-inspired algorithms using previous literature guidelines (Molina et al. 2020). ...
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In the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing operational efficiency and safety. They must travel in crowded work areas and cross narrow corridors with strict safety and time requirements. Bio‐inspired optimization algorithms have emerged as a promising approach to deal with complex optimization scenarios. Thus, this paper explores the ability of three novel bio‐inspired algorithms: the Bat Algorithm (BA), the Whale Optimization Algorithm (WOA) and the Gazelle Optimization Algorithm (GOA); to optimise the AGV path planning in complex environments. To do it, a new optimization strategy is described: the AGV trajectory is based on clothoid curves and a specialised piece‐wise fitness function which prioritises safety and efficiency is designed. Simulation experiments were conducted across different occupancy maps to evaluate the performance of each algorithm. WOA demonstrates faster optimization providing suitable safety solutions 4 times faster than GOA. Meanwhile, GOA gives solutions with better safety metrics but demands more computational time. The study highlights the potential of bio‐inspired approaches for AGV trajectory optimisation and suggests avenues for future research, including hybrid algorithm development.
... Several of these contributions have been continuously updated over time, leading to more sophisticated taxonomies that incorporate descriptions of novel overviews and methodologies. A notable example is [19], whose latest version maintained in [20] has systematically collected, analyzed, and classified more than 500 bioinspired solvers to date. ...
... The plethora of bioinspired algorithms available poses a significant challenge in choosing the best solver for an optimization problem. In this context, the work of Molina et al. [19] proposes a dual taxonomy according to their inspiration and algorithmic behavior. They highlight that: ...
... Later, the work in [33] discussed this phenomenon in relation to the number of papers published, arguing that "the new population-based natureinspired algorithms are released every month and, basically, they have nothing special and no novel features for science". Their conclusions are aligned with the taxonomy published in [19], because "our research revealed that the process of the new population-based nature-inspired algorithm possesses the behavior of the swarm intelligence paradigm", revealing that the category with the highest number of contributions is Swarm Intelligence. ...
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Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in nature, like evolution itself, these algorithms offer innovative solutions beyond the reach of traditional optimization methods. They excel at finding near-optimal solutions in large, complex search spaces, making them invaluable in numerous fields. However, both areas are plagued by challenges at their core, including inadequate benchmarking, problem-specific overfitting, insufficient theoretical grounding, and superfluous proposals justified only by their biological metaphor. This overview recapitulates and analyzes in depth the criticisms concerning the lack of innovation and rigor in experimental studies within the field. To this end, we examine the judgmental positions of the existing literature in an informed attempt to guide the research community toward directions of solid contribution and advancement in these areas. We summarize guidelines for the design of evolutionary and bioinspired optimizers, the development of experimental comparisons, and the derivation of novel proposals that take a step further in the field. We provide a brief note on automating the process of creating these algorithms, which may help align metaheuristic optimization research with its primary objective (solving real-world problems), provided that our identified pathways are followed. Our conclusions underscore the need for a sustained push towards innovation and the enforcement of methodological rigor in prospective studies to fully realize the potential of these advanced computational techniques.
... This demonstrates the versatility of these algorithms in solving complex optimization problems (16), including those related to air taxi services. Overall, the literature suggests that the application of metaheuristic algorithms such as Grey Wolf Optimization Algorithm and Ant Colony Optimization Algorithm can significantly improve the optimization of air taxi services (19)(20)(21)(22)(23)(24)(25). Further research in this area is needed to explore the full potential of these algorithms in enhancing the efficiency and sustainability of air taxi operations. ...
... Special consideration should be paid to dealing with such a discrete problem. In this regard, proper representation of design variables is addressed by direct index coding when evolutionary algorithms are applied [4][5][6][7]. However, some other metaheuristic algorithms fall in the category of directional search methods that generate continuous positions during their search [8][9][10][11][12]. ...
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Diagrids are of practical interest in high-rise buildings due to their architectural configuration and efficiency in withstanding lateral loads by exterior diagonal members. In the present work, diagrid models are screened based on a sizing optimization approach. Section index of each member group is treated as a discrete design variable in the optimization problem to be solved. The structural constraints are evaluated due to Load and Resistant Design Factor regulations under both gravitational and wind loadings. The research is threefold: first, falcon optimization algorithm is utilized as a meta-heuristic paradigm for such a large-scale and highly constrained discrete problem. Second, the effect of geometry variation in diagrids on minimal structural weight is studied for 18 diagrid models via three different heights (12, 20 and 30 stories) and three diagrid angles. Third, distinct cases of rigid and flexible bases are compared to study the effect of such boundary conditions on the results. The effect of soil flexibility beneath the foundation on the optimal design was found highly dependent on the diagrid geometry. The best weight and performance in most of the treated examples belong to the geometry that covers two stories by every grid line on the flexible-base.
... The diagram centre has the largest cluster of terms. Keywords like "Portfolio selection", "transaction cost" (Mellal et al., 2020), "Investment analysis" (Hayes, 2021), and "Behavioural finance" (Molina et al., 2020) are included in this cluster. However, a cluster of keywords " Montecarlo simulation" (Ghodrati & Zahiri, 2014), "copulas" (Deng et al., 2011), "conditional value at risk" (Pinar, 2013), "meanvariance" (Alexander & Baptista, 2002) and "value at risk" (Pinar, 2013) are found among them. ...
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Purpose: This study provides a comprehensive analysis of the evolution of portfolio optimization over the last three decades, employing systematic review and advanced bibliometric techniques to map key trends, influential works, and significant contributors in the field. Design/Methodology/Approach: Adhering to PRISMA guidelines, we conducted a systematic review and bibliometric analysis of 1,000 articles sourced from the Web of Science database, spanning from 1989 to 2023. Advanced bibliometric tools, including citation analysis, co-occurrence analysis, and network visualization, were utilized to identify prominent authors, influential journals, and emerging research themes. Findings: Our analysis reveals a significant growth in portfolio optimization literature, particularly in recent years. Key findings include the identification of pivotal authors, foundational papers, and leading journals that have shaped the field. The study also traces the methodological evolution from traditional models, like Markowitz's Modern Portfolio Theory, to contemporary approaches incorporating artificial intelligence and machine learning. Practical Implications: This study offers valuable insights for researchers and practitioners by highlighting critical developments in portfolio optimization. It also suggests areas for future research, particularly in integrating advanced data analytics and AI-driven methodologies into portfolio management. Originality/Value: This paper stands out by combining systematic review with a comprehensive bibliometric analysis, offering a holistic view of the portfolio optimization landscape. It not only synthesizes past research but also identifies emerging trends and gaps, providing a foundation for future explorations in this dynamic field.
... According to the meta-heuristics metaphor by Molina, D. It has been categorized based on incorporates methods used in physics, swarm intelligence, breeding, and chemically. This structure has provided a broad review of several metaphor-inspiring techniques [54]. The general meta-heuristics classification is shown in Fig. 4 since the combination and stigmergy are types of solution creation whereas all population, representative-based, sub-population, and neighborhood are types of differential vector movement. ...
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Numerous processing and storage resources are available through pay-per-use cloud computing. Cloud resources are managed by data centers based on demand, availability, and other factors like reliability and security. Due to task size and workflow interdependence, task scheduling is a complex process that impacts overall system performance. By considering factors like cost, failure rate, and makespan that influence task scheduling, the goal is to achieve optimal task scheduling among the resources. Meta-heuristics strategies are used extensively in research to solve task-scheduling issues. This study presents an overview of meta-heuristics in general and a comparative analysis of swarm intelligence-based meta-heuristic algorithms used in cloud task scheduling. It has been observed that scheduling performance has been enhanced by leveraging the advantages of diverse meta-heuristic algorithms in hybrid methods. The different meta-heuristic algorithms, environments, simulation tools, scheduling objectives, and metrics that go along with them are compared.
... The main representative techniques within these streams are the genetic algorithm (GA, [21,22]), particle swarm optimization (PSO, [23]), and ant colony optimization (ACO, [24]). Being more specific, it was PSO, thanks to its overwhelming success and novelty, the one that decisively influenced the creation of a plethora of bio-inspired methods, which clearly inherit its main philosophy [25]. ...
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In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.
... Context The theoretical framework of this study is based on the idea about Naturalistic Intelligence and New Environmental Paradigm (NEP) (Platje et al. 2022). Molina et al. (2020) states that Naturalistic Intelligence is the ability to classify flora and fauna, which contributes critical understanding of supporting a wider interest for our environment. The NEP, meanwhile paints a very holistic picture of environment taking into account the connectivity between all species to each other and argues for an ethical responsibility towards protecting our ecology. ...
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Objective: Drawing up on naturalistic intelligence, the New Environmental Paradigm and environmental sensitivity concepts, this paper aims to investigate their relationships among high school students in terms of differences regarding significant connections of these relations hiop with each other within limits occured by socio-demographic variables through considering substantial environments from context-based higher education oriented phenomenon. Methods: We employed a quantitative approach using path analysis to test the direct and indirect effects of naturalistic intelligence, NEP on ESI. Among students responses were collected using a validated questionnaire which was statistically analyzed to find the significance of relations. Finding: A significant positive correlation existed between the New Environmental Paradigm and environmental sensitivity, but did not find a significant correlation of naturalistic intelligence with it. Results indicate that instilling a pro-environmental attitude is essential in increasing environmental sensitivity to adolescents. Novelty: This study presents a novel perspective by emphasizing the New Environmental Paradigm's role in shaping high school students' environmental attitudes, advocating for value-based education over mere skill enhancement. Conclusion: Our findings underscore the importance of instilling environmentally friendly values and attitudes, rather than just naturalistic skills when designing educational programs. Educators can cultivate environmental citizens ready to meet current challenges by including experiential learning with critical thinking into curricula.