Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems
Neural and Evolutionary Computing
2021-01-27 v1 Optimization and Control
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.
Cite
@article{arxiv.2101.10936,
title = {Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems},
author = {Carwyn Pelley and Mauro S. Innocente and Johann Sienz},
journal= {arXiv preprint arXiv:2101.10936},
year = {2021}
}
Comments
Preprint submitted to the 8th ASMO UK Conference on Engineering Design Optimization