A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning

Abstract

A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning.