TL;DR
A novel evolutionary algorithm termed CMOEA-TSD is proposed, which adopts a three-population, two-stage co-evolutionary framework to enhance diversity and coordinate multiple search behaviors and significantly outperforms seven state-of-the-art algorithms.
Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective opti...
A dual-population MOEA with Exploration and Exploitation Decoupled (MOEA/EED) is proposed: the exploration population uses a simple aging mechanism, while the exploitation population preserves the currently optimal solutions.
Large-scale multi-objective optimization problems involve high-dimensional decision variables, vast search spaces, and conflicting objectives, which often lead to slow convergence, premature convergence, and poorly distributed solutions when conventional evolutionary algorithms operate under limited evaluation budgets....
A heterogeneous population co-evolutionary algorithm (HPCEA) tailored for sparse LSMOPs is proposed, and extensive experiments against six state-of-the-art algorithms across eight benchmark suites and three real-world scenarios demonstrate HPCEA’s superiority.
An information-assisted solution generation based dual population constrained multi-objective evolutionary algorithm, called GSCMO, which dynamically optimizes the mutation strength, focusing on enhancing solution diversity and maintaining extensive exploration of the population in the decision space, thereby improving...
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