Improving the performance of metaheuristic algorithms through structured populations and evolutionary game theory
DOI:
https://doi.org/10.32870/recibe.v15i2.495Keywords:
metaheuristics; game theory; optimization; competition; Metropolis–HastingsAbstract
Diversity plays a fundamental role in metaheuristic algorithms, as it helps prevent premature convergence, maintains a balance between exploration and exploitation, and reduces the likelihood of being trapped in local optima. Many traditional metaheuristic algorithms rely on a single strategy to generate new solutions, which can limit the diversity of the population. In contrast, incorporating multiple strategies enables different search behaviors and produces a broader set of candidate solutions, thereby improving the exploration of the search space. Evolutionary game theory introduces adaptive mechanisms in which agents modify their strategies through competitive interactions, reinforcing successful strategies while discarding less effective ones. Structured populations, unlike unstructured ones, help preserve strategic diversity through localized competition, where each individual interacts only with a subset of the population rather than with all individuals. In this work, a novel metaheuristic method based on evolutionary game theory applied to structured populations is proposed. Initially, individuals are positioned near promising regions using the Metropolis–Hastings algorithm. Subsequently, each individual is assigned a specific search strategy, and the population is divided into several clusters. Within these clusters, strategies evolve through intra-cluster competition to improve search efficiency and solution quality. The proposed approach was evaluated using 30 benchmark functions and compared with several well-known metaheuristic algorithms. The results demonstrate improvements in both solution quality and convergence speed.References
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