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

• Published in: The Journal of supercomputing Vol. 80; no. 9; pp. 12346 - 12407
• Main Authors: Gao, Yuansheng, Zhang, Jiahui, Wang, Yulin, Wang, Jinpeng, Qin, Lang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2024; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s11227-024-05905-4

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 .