Gravitational Locally Informed Particle Swarm Algorithm for solving Multimodal Optimization Problems

Locally Informed Particle Swarm (LIPS) is a simple and effective method for solving multimodal optimization problems. Despite the good performance of LIPS’s velocity updating rule, the quality (fitness) of this local neighbors is not considered in calculating the velocity. Considering the quality of neighbors to update the particle velocity can reinforce the search power of LIPS. In this paper, a new version of LIPS with Gravitational velocity updating rule (GLIPS) is proposed. In GLIPS each particle successively adjusts its position towards the best positions of its local neighbors using laws of gravity and motion. In proposed GLIPS, local neighbors with a higher quality get a greater gravitational mass and therefore are allowed to apply the higher gravity force to other particles to attract them. In this case, the particles near good solutions try to attract the other particles which are exploring the search space. We perform a detailed empirical evaluation on the several commonly used multimodal benchmark functions. Our results demonstrate that the new velocity updating rule for LIPS can obtain better results for multimodal function optimization.