An improved multimodal PSO method based on electrostatic interaction using n- nearest-neighbor local search
This is an incremental improvement for researchers in optimization algorithms, addressing particle attenuation and neighbor-following issues in multimodal problems.
The paper tackled multimodal optimization by proposing LSEPSO, an improved algorithm combining Electrostatic Particle Swarm Optimization with a modified n-nearest-neighbor local search, which outperformed other tested algorithms on benchmark functions.
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.