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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Veröffentlicht in:The Journal of supercomputing Jg. 80; H. 9; S. 12346 - 12407
Hauptverfasser: Gao, Yuansheng, Zhang, Jiahui, Wang, Yulin, Wang, Jinpeng, Qin, Lang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2024
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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 .
Bibliographie:ObjectType-Article-1
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-05905-4