Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers

TL;DR

This paper introduces a pseudo-adaptive penalization scheme for particle swarm optimizers that maintains constant coefficients but adaptively relaxes constraint tolerances, improving constraint handling without problem-specific tuning.

Contribution

It proposes a novel pseudo-adaptive penalization method that relaxes constraint tolerances dynamically, avoiding the need for problem-specific penalization coefficient tuning.

Findings

Pseudo-adaptive relaxation improves constraint handling.

Method performs well across benchmark problems.

Compared with existing adaptive schemes.

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.