Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search

Particle swarm optimization (PSO) has shown a good performance on solving global optimization problems. Traditional PSO has two main drawbacks of premature convergence and low convergence speed, especially on complex problems. This paper presents a new approach called Adaptive multi-layer particle swarm optimization with neighborhood search (AMPSONS), where the traditional PSO is improved by employing an adaptive multi-layer search and neighborhood search strategy to achieve a trade-off between exploitation and exploration abilities. In order to evaluate the performance of the proposed AMPSONS algorithm, the performance of AMPSONS is compared with five other PSO family algorithms, namely, CLPSO, DNLPSO, DNSPSO, global MLPSO and local MLPSO on a set of benchmark functions. The comparison results show that AMPSONS has a promising performance on majority of the test functions.