Love Evolution Algorithm: a stimulus–value–role theory-inspired evolutionary algorithm for global optimization

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the o...

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Vydáno v:	The Journal of supercomputing Ročník 80; číslo 9; s. 12346 - 12407
Hlavní autoři:	Gao, Yuansheng, Zhang, Jiahui, Wang, Yulin, Wang, Jinpeng, Qin, Lang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.06.2024 Springer Nature B.V
Témata:	Behavior Benchmarks Chemical reactions Chemistry Classification Compilers Computer Science Evolution & development Evolutionary algorithms Genetic algorithms Global optimization Heuristic methods Interpreters Mathematics Optimization algorithms Optimization techniques Physics Processor Architectures Programming Languages Rank tests Love Evolution Algorithm Metaheuristic Evolutionary algorithm Optimization
ISSN:	0920-8542, 1573-0484
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Abstract	This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm .
AbstractList	This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm .
Author	Gao, Yuansheng Wang, Yulin Zhang, Jiahui Wang, Jinpeng Qin, Lang
Author_xml	– sequence: 1 givenname: Yuansheng surname: Gao fullname: Gao, Yuansheng email: gaoyuansheng2021@163.com organization: College of Science, Liaoning Technical University – sequence: 2 givenname: Jiahui surname: Zhang fullname: Zhang, Jiahui organization: College of Mining Engineering, Liaoning Technical University – sequence: 3 givenname: Yulin surname: Wang fullname: Wang, Yulin organization: College of Electrical and Control Engineering, Liaoning Technical University – sequence: 4 givenname: Jinpeng surname: Wang fullname: Wang, Jinpeng organization: College of Science, Liaoning Technical University – sequence: 5 givenname: Lang surname: Qin fullname: Qin, Lang organization: School of Mechanics and Engineering, Liaoning Technical University
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Snippet	This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of...
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SubjectTerms	Behavior Benchmarks Chemical reactions Chemistry Classification Compilers Computer Science Evolution & development Evolutionary algorithms Genetic algorithms Global optimization Heuristic methods Interpreters Mathematics Optimization algorithms Optimization techniques Physics Processor Architectures Programming Languages Rank tests
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Title	Love Evolution Algorithm: a stimulus–value–role theory-inspired evolutionary algorithm for global optimization
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