Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems

TL;DR

This paper presents a hybrid optimization approach combining Particle Swarm Optimization with SQP local search to improve constrained optimization, demonstrating competitive performance on benchmark problems.

Contribution

It introduces a novel hybrid method that enhances global search with local refinement, showing improved results over standard PSO algorithms.

Findings

Hybrid GP-PSO with SQP outperforms standard PSO algorithms.

The local search significantly improves solution quality.

The method is effective on benchmark constrained optimization problems.

Abstract

The combining of a General-Purpose Particle Swarm Optimizer (GP-PSO) with Sequential Quadratic Programming (SQP) algorithm for constrained optimization problems has been shown to be highly beneficial to the refinement, and in some cases, the success of finding a global optimum solution. It is shown that the likely difference between leading algorithms are in their local search ability. A comparison with other leading optimizers on the tested benchmark suite, indicate the hybrid GP-PSO with implemented local search to compete along side other leading PSO algorithms.