December 1, 2025Open Access

Comparative evaluation of large-scale many objective algorithms on complex optimization problems

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Evaluation reveals performance differences in many-objective algorithms, suggesting enhanced convergence and diversity in optimization.

Performance metrics showed significant differences across many-objective algorithms in optimization tasks, highlighting their strengths and weaknesses.
A benchmarking approach assessed algorithms such as LMEA, LMOCSO, and S3CMAES across various large-scale problems with differing objectives.
Results indicated improvements in convergence and diversity for Hybrid-LMEA in high-dimensional optimization settings.
Findings support the informed selection of algorithms based on problem characteristics in many-objective optimization.

Chaudhary et al. (2025) studied this question.

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