In many-objective optimization problems (MaOPs), forming sound tradeoffs between convergence and diversity for the environmental selection of evolutionary algorithms is a laborious task. In ...particular, strengthening the selection pressure of population toward the Pareto-optimal front becomes more challenging, since the proportion of nondominated solutions in the population scales up sharply with the increase of the number of objectives. To address these issues, this paper first defines the nondominated solutions exhibiting evident tendencies toward the Pareto-optimal front as prominent solutions, using the hyperplane formed by their neighboring solutions, to further distinguish among nondominated solutions. Then, a novel environmental selection strategy is proposed with two criteria in mind: 1) if the number of nondominated solutions is larger than the population size, all the prominent solutions are first identified to strengthen the selection pressure. Subsequently, a part of the other nondominated solutions are selected to balance convergence and diversity and 2) otherwise, all the nondominated solutions are selected; then a part of the dominated solutions are selected according to the predefined reference vectors. Moreover, based on the definition of prominent solutions and the new selection strategy, we propose a hyperplane assisted evolutionary algorithm, referred here as hpaEA , for solving MaOPs. To demonstrate the performance of hpaEA , extensive experiments are conducted to compare it with five state-of-the-art many-objective evolutionary algorithms on 36 many-objective benchmark instances. The experimental results show the superiority of hpaEA which significantly outperforms the compared algorithms on 20 out of 36 benchmark instances.
Interactive evolutionary computation (IEC) has demonstrated significant success in addressing numerous real-world problems that are challenging to quantify mathematically or are inadequately ...evaluated using conventional computational models. This success arises from IEC’s ability to effectively amalgamate evolutionary computation (EC) algorithms with expert knowledge and user preferences. These problems encompass the creative and personalized generation of products, art, and sound; the design optimization of communication systems, environments, and pharmaceuticals; and expert support in areas such as portfolio selection and hearing aid fitting, among others. Despite significant advancements in IEC over the past two decades, no major comprehensive survey encompassing all aspects of IEC research has been conducted since 2001. This article aims to address this gap by providing a comprehensive survey and an enriched definition and scope of IEC, along with innovative ideas for future research in this field. The proposed IEC definition more clearly reflects the mechanism and current research status of the IEC. Additionally, the survey categorizes IEC research into five distinct directions from a problem-oriented perspective: interactive evolutionary computation algorithms, IEC algorithm improvements, evolutionary multi-objective optimization (EMO) with IEC, human perception studies with IEC, and IEC applications. Each direction is meticulously explored, elucidating its contents and key features, while providing a concise summary of pertinent IEC studies. Finally, the survey investigates several promising future trends in IEC, analyzing them through the lens of these five directions and considering the current perspective of computational intelligence, artificial intelligence, and human-machine interaction.
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•A comprehensive survey of interactive evolutionary computation over the last two decades.•A new interactive evolutionary computation definition.•Five existing interactive evolutionary computation development directions.•Several promising future trends of interactive evolutionary computation in the field of artificial intelligence.
In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving ...complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
•Atomic Orbital Search is proposed as a novel metaheuristic algorithm.•Benchmark functions are tested and engineering design problems are solved.•Comparative analyses is conducted for performance ...evaluation of the new algorithm.•Statistical analysis is conducted for competitive investigation of the new algorithm.
In this paper, the Atomic Orbital Search (AOS) is proposed as a novel metaheuristic algorithm for optimization purposes. The main concept of this algorithm is based on some principles of quantum mechanics and the quantum-based atomic model in which the general configuration of electrons around nucleus is in perspective. In order to evaluate the performance of this algorithm, a total number of 20 unconstrained mathematical test functions are utilized with different dimensions of 2–100 while a maximum number of 150,000 function evaluations is considered with 100 independent optimization runs for statistical purposes. A complete statistical analysis is also conducted by utilization of the Kolmogorov Smirnov, Wilcoxon and the Kruskal Wallis tests while 8 metaheuristics are also utilized as alternatives for comparative purposes. The latest Competitions on Evolutionary Computation (CEC) regarding the single objective real-parameter numerical optimization (CEC 2017) including 30 benchmark test functions is also considered in which the capability of the proposed algorithm is compared to the most state−of-the−art algorithms in the optimization field. In addition, a total number of 5 constrained engineering design problems are utilized as design examples including some of the constrained optimization problems of the recent Competitions on Evolutionary Computation (CEC 2020). The obtained results of the AOS algorithm in dealing with the constraint problems are compared to the results of different standard, improved and hybrid metaheuristic algorithms from the literature. The obtained results demonstrate that the proposed AOS algorithm provides very outstanding results in dealing with the mathematical and engineering design problems.
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