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Multi-objective differential evolution algorithm integrating a directional generation mechanism for multi-objective optimization problems.

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Název: Multi-objective differential evolution algorithm integrating a directional generation mechanism for multi-objective optimization problems.
Autoři: Yuan, Zhuoxuan1 (AUTHOR), Ouyang, Haibin1 (AUTHOR) oyhb1987@163.com, Li, Steven2 (AUTHOR), Houssein, Essam H.3,4 (AUTHOR), Samee, Nagwan Abdel5 (AUTHOR)
Zdroj: Applied Soft Computing. Dec2025:Part B, Vol. 184, pN.PAG-N.PAG. 1p.
Témata: Multi-objective optimization, Differential evolution, Algorithms, Heterogeneity
Abstrakt: Multi-objective Evolutionary Algorithms (MOEAs) have gained significant attention due to their effectiveness in solving multi-objective optimization problems. However, when dealing with complex problems, they often face challenges such as low convergence accuracy and poor diversity. To address these issues, we propose a novel multi-objective differential evolution algorithm, MODE-FDGM, which integrates a directional generation mechanism. The key contributions are: (1) A directional-generation method leverages current and past information to rapidly build feasible solutions, boosting both speed and quality in exploring Pareto non-dominated space; (2) An update mechanism that combines crowding distance evaluation, iterating the population and incorporating historical information to enhance diversity and improve the ability to escape local optima; and (3) The introduction of an ecological niche radius concept along with a dual-mutation ecological niche selection evolution strategy, which improves exploration of unexplored spaces and preserves population diversity. Comparative experiments against 7 algorithms, including both classical and contemporary ones, on 24 benchmark functions demonstrate that the proposed algorithm markedly enhances the exploration of Pareto non-dominated solutions, exhibiting superior performance and advanced capabilities. • We propose a novel multi-objective differential evolution algorithm, MODE-FDGM, which integrates a directional generation mechanism. The key contributions are: • Directional generation mechanism constructs solutions, guiding the search toward a superior non-dominated Pareto front. • Update mechanism uses crowding distance & historical info to enhance diversity & escape local optima. • Ecological niche radius & dual-mutation strategy explore uncharted areas while preserving population diversity. [ABSTRACT FROM AUTHOR]
Databáze: Supplemental Index
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Abstrakt:Multi-objective Evolutionary Algorithms (MOEAs) have gained significant attention due to their effectiveness in solving multi-objective optimization problems. However, when dealing with complex problems, they often face challenges such as low convergence accuracy and poor diversity. To address these issues, we propose a novel multi-objective differential evolution algorithm, MODE-FDGM, which integrates a directional generation mechanism. The key contributions are: (1) A directional-generation method leverages current and past information to rapidly build feasible solutions, boosting both speed and quality in exploring Pareto non-dominated space; (2) An update mechanism that combines crowding distance evaluation, iterating the population and incorporating historical information to enhance diversity and improve the ability to escape local optima; and (3) The introduction of an ecological niche radius concept along with a dual-mutation ecological niche selection evolution strategy, which improves exploration of unexplored spaces and preserves population diversity. Comparative experiments against 7 algorithms, including both classical and contemporary ones, on 24 benchmark functions demonstrate that the proposed algorithm markedly enhances the exploration of Pareto non-dominated solutions, exhibiting superior performance and advanced capabilities. • We propose a novel multi-objective differential evolution algorithm, MODE-FDGM, which integrates a directional generation mechanism. The key contributions are: • Directional generation mechanism constructs solutions, guiding the search toward a superior non-dominated Pareto front. • Update mechanism uses crowding distance & historical info to enhance diversity & escape local optima. • Ecological niche radius & dual-mutation strategy explore uncharted areas while preserving population diversity. [ABSTRACT FROM AUTHOR]
ISSN:15684946
DOI:10.1016/j.asoc.2025.113791