Performance evaluation of dynamic multi-swarm particle swarm optimizer with different constraint handling methods on path planning problems

One of the aims of path planning in a known environment with static obstacles is to find an optimized curve which does not intersect with obstacles. In this paper, two different constraints handling methods are combined into dynamic multi-swarm particle swarm optimizer with crossover operator to improve its performance on the path planning problems based on Bezier curves. From the results, the dynamic penalty function performs better comparing with static penalty function and dynamic threshold ε.