Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers

Mauro S. Innocente; Johann Sienz

doi:10.4203/ccp.93.123

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arXiv · 2101.11441

Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers

Mauro S. Innocente·Johann Sienz

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Abstract

The penalization method is a popular technique to provide particle swarm optimizers with the ability to handle constraints. The downside is the need of penalization coefficients whose settings are problem-specific. While adaptive coefficients can be found in the literature, a different adaptive scheme is proposed in this paper, where coefficients are kept constant. A pseudo-adaptive relaxation of the tolerances for constraint violations while penalizing only violations beyond such tolerances results in a pseudo-adaptive penalization. A particle swarm optimizer is tested on a suite of benchmark problems for three types of tolerance relaxation: no relaxation; self-tuned initial relaxation with deterministic decrease; and self-tuned initial relaxation with pseudo-adaptive decrease. Other authors' results are offered as frames of reference.

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BibTeX RIS

Mauro S. Innocente, Johann Sienz. 2021-01-25. Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers. https://doi.org/10.4203/ccp.93.123

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