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

Year: 2021

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2101.10936

Authors: Pelley, Carwyn, Innocente, Mauro S., Sienz, Johann

Institution: Swansea University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2803548837

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