An improved multimodal PSO method based on electrostatic interaction using n- nearest-neighbor local search

TL;DR

This paper introduces LSEPSO, an enhanced multimodal optimization algorithm combining electrostatic particle swarm optimization with a local search that uses n-nearest neighbors, improving global and local optima detection.

Contribution

The paper presents a novel modification of EPSO with an n-nearest-neighbor local search, enhancing its ability to find multiple optima in multimodal problems.

Findings

LSEPSO outperforms other algorithms on benchmark functions.

The n-nearest-neighbor local search improves optima diversity.

The method effectively prevents particle attenuation.

Abstract

In this paper, an improved multimodal optimization (MMO) algorithm,called LSEPSO,has been proposed. LSEPSO combined Electrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then made some modification on them. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which used n-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it tried to find a point which could be the alternative of particle's personal best. This method prevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstrated that the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.