Abstract: Particle swarm optimization(PSO) algorithm relies on the cooperation between particles,which makes it show great intelligence in solving many optimization problems.However,due to the optimization mechanism,particles are easy to break through the boundary restrictions of the feasible region.If this behavior can have a clear guiding significance in the optimization process,it will help to improve the optimization performance of the algorithm.More importantly,the learning objects of particles in the original particle swarm optimization algorithm are mainly focused on the global optimal particles.This updating mechanism undoubtedly accelerates the loss of population diversity,and makes the population tend to fall into the local optimal.In order to further improve the population diversity and convergence accuracy when solving complex problems,an elite interactive learning particle swarm optimization algorithm(A-EIPSO) based on adaptive strategy is proposed.Firstly,the algorithm introduces a new bound-handling technique into the original PSO algorithm,and adaptively endows the distribution characteristics of particles in the solution space by using the historical location information and the distance of out of bounds particles,so as to modify the position of particles to meet the requirements of effectively handling out of violated particles.Then,based on multi-swarm technology,an elites learning strategy is designed to promote the exchange of social information among subswarms,and the elite particles instead of the global optimal particles guide the optimization behavior of particles in each subswarm.Experimental results show that,in most cases,the adaptive strategy can ensure that particles can achieve uniform exploration in the search space and significantly improve the performance of PSO algorithm.In addition,A-EIPSO is compared with five advanced particle swarm optimization variant algorithms and two mainstream evolutionary algorithms on the CEC2017 benchmark suite.The results show that A-EIPSO has superior performance on different types of functions,improves the convergence accuracy of most optimization pro-blems,and is superior to other representative PSO variant algorithms and evolutionary algorithms.

Key words: Particle swarm optimization algorithm, Adaptive strategy, Bound-handling techniques, Multi-swarm, Elite Interactive learning