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This is a Experimental Study study.

Linear Ensemble Algorithm: A Novel Meta-Heuristic Approach for Solving Constrained Engineering Problems

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This approach integrates local surrogates with evolutionary search, generating high-quality solutions despite computational challenges.

LEAL achieves 55–85% lower mean error compared to traditional algorithms like GA and PSO.
For multi-objective problems, LEAL generates feasible Pareto fronts but often performs slower than NSGA-II.
LEAL maintains local surrogate ensembles, resulting in higher resource usage than lightweight alternatives.
Future work focuses on enhancing efficiency and expanding the algorithm's applicability to larger, dynamic challenges.

Wongsrisai et al. (2025) studied this question.

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View Full Paper

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Authors

This approach integrates local surrogates with evolutionary search, generating high-quality solutions despite computational challenges.

LEAL achieves 55–85% lower mean error compared to traditional algorithms like GA and PSO.
For multi-objective problems, LEAL generates feasible Pareto fronts but often performs slower than NSGA-II.
LEAL maintains local surrogate ensembles, resulting in higher resource usage than lightweight alternatives.
Future work focuses on enhancing efficiency and expanding the algorithm's applicability to larger, dynamic challenges.

Wongsrisai et al. (2025) studied this question.

View Full Paper

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