Hybrids of two-subpopulation PSO algorithm with local search methods for continuous optimization

Abstract

The paper studies the problem of continuous function optimization. Proposed are twelve hybrids of known methods such as Particle Swarm Optimization (also in a two-subpopulation version), quasi-Newton method and Nelder-Mead method. Described modifications are introduced in order to improve performance and increase the accuracy of known methods. Algorithms are tested against eight benchmark functions and compared with classical versions of: Particle Swarm Optimization algorithm, Newton's, quasi-Newton and Nelder-Mead methods. Presented results allow to indicate two methods that perform satisfactorily in most cases.