A Novel Optimization Approach Based on Unstructured Evolutionary Game TheoryA Novel Optimization Approach Based on Unstructured Evolutionary Game Theory
DOI:
https://doi.org/10.32870/recibe.v13i1.332Keywords:
Optimización, Competición, Metaheurística, Teoría de Juegos, Metropolis-HastingAbstract
Proposing new metaheuristic methods is crucial for the continuous improvement of algorithm development and for effectively addressing increasingly complex real-world optimization problems. On the other hand, Evolutionary Game Theory examines how competition can modify the strategies of individuals within a population, enabling successful mechanisms to spread while reducing or eliminating less successful strategies. This article presents a novel optimization approach based on the principles of Evolutionary Game Theory. In the proposed method, all individuals are initialized using the Metropolis–Hastings (MH) technique, which places candidate solutions at starting points closer to the optimal or near-optimal regions of the problem. An original strategy is assigned to each individual in the population. By considering the interactions and competition among the various agents involved in the optimization problem, the proposed approach adapts strategies to improve search efficiency and discover higher-quality solutions. To evaluate its performance, the method was compared with eight well-known metaheuristic algorithms using 30 benchmark test functions. The proposed methodology demonstrated superior performance in terms of solution quality, scalability across dimensions, and convergence speed when compared with the other approaches.References
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