A modified multi-objective particle swarm optimisation with entropy adaptive strategy and Levy mutation in the internet of things environment

摘要

For multi-objective optimisation problems, a balance between convergence and diversity in multi-objective particle swarm algorithms is the key to approach real Pareto fronts with well-distributed. In order to obtain the Pareto optimal set with good distribution, a multi-objective particle swarm optimisation algorithm based on Levy mutation and information entropy is proposed in this paper. Firstly, an entropy adaptive strategy is proposed to balance the exploration and exploitation ability of the swarm, which guides the flight direction of particles via the adaptively adjusting parameters with information entropy of the swarm. Secondly, a Levy mutation operator is proposed to ensure that the algorithm has the ability to jump out of the local solution. The new mutation operator can control the magnitude of particle mutation by random steps, so that the mutation is more anisotropic and diverse, thus ensuring that the particles still have a large global exploration ability in the late iteration as well as increasing the local exploitation accuracy. Finally, the experimental results in benchmark test functions show that the proposed algorithm has better exploration ability than several compared algorithms and can approach real Pareto front with better distribution.