Adaptive Mutation Opposition-Based Particle Swarm Optimization

To solve the problem of premature convergence in traditional particle swarm optimization (PSO), This paper proposed a adaptive mutation opposition-based particle swarm optimization (AMOPSO). The new algorithm applies adaptive mutation selection strategy (AMS) on the basis of generalized opposition-based learning method (GOBL) and a nonlinear inertia weight (AW). GOBL strategy can provide more chances to find solutions by space transformation search and thus enhance the global exploitation ability of PSO. However, it will increase likelihood of being trapped into local optimum. In order to avoid above problem, AMS is presented to disturb the current global optimal particle and adaptively gain mutation position. This strategy is helpful to improve the exploration ability of PSO and make the algorithm more smoothly fast convergence to the global optimal solution. In order to further balance the contradiction between exploration and exploitation during its iteration process, AW strategy is introduced. Through compared with several opposition-based PSOs on 14 benchmark functions, the experimental results show that AMOPSO greatly enhance the performance of PSO in terms of solution accuracy, convergence speed and algorithm reliability.