An ensemble local search framework for population-based metaheuristic algorithms on single-objective optimization

Abstract

Population-based metaheuristic algorithms are recognized as potent tools for tackling complex optimization problems. However, they are often plagued by premature convergence, making them susceptible to getting trapped in local optima. To mitigate this issue, this paper introduces an Ensemble Local Search (ENLS) framework that can seamlessly integrate with various metaheuristic algorithms. In ENLS, an online detection mechanism is designed to identify the occurrence of premature convergence during the search process. Subsequently, three local search strategies are triggered to assist the following three subpopulations based on the characteristic of each one: (1) the orthogonal learning mechanism is applied to design an orthogonal crossover-based local search strategy for the superior subpopulation, focusing on refining solutions within a narrow region; (2) a dynamic Lévy flight-based local search strategy is developed for the medium subpopulation to enhance the population diversity by leveraging the long-term short-step Lévy random walking; (3) the inferior subpopulation employs an opposition-based local search, incorporating a modified opposition-based learning mechanism to explore a broader space between inferior solutions and their opposite positions. By integrating these three local search strategies, the ENLS framework can effectively balance exploration and exploitation, addressing the challenge of premature convergence. To validate the effectiveness of ENLS, comparative experiments are conducted using 30 benchmark problems from the IEEE CEC 2014 test suite and 20 real-world optimization problems. The experimental results confirm that the ENLS framework significantly enhances the optimization capabilities of the considered 12 metaheuristic algorithms without significantly increasing runtime complexity.