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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Bibliographic Details
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
Online Access:	Get full text
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