A novel adaptive sequential niche technique for multimodal function optimization

This paper proposes a novel adaptive sequential niche particle swarm optimization (ASNPSO) algorithm, which uses multiple sub-swarms to detect optimal solutions sequentially. In this algorithm, the hill valley function is used to determine how to change the fitness of a particle in a sub-swarm run currently. This algorithm has strong and adaptive searching ability. The experimental results show that the proposed ASNPSO algorithm is very effective and efficient in searching for multiple optimal solutions for benchmark test functions without any prior knowledge.