TL;DR
A reference vector-based diversity-enhancing strategy in the environmental selection of the diversity-enhancing auxiliary population is proposed for the main population, which assists the population in progressively traversing vast infeasible regions by dynamically activating constraints step by step.
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.
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....
TSEA-SR is proposed, a two-stage MaOEA with survival-ratio-based switching, in which the stage transition is determined by offspring survival after environmental selection, and the results demonstrate the competitive performance of TSEA-SR across the evaluated problems.
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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