A self-adapting improvement Particles Swarm Optimization algorithm and application in Multi-Criteria Group Decision-making

In order to avoid the phenomenon such as earliness and convergence emerged by Particles Swarm Optimization (PSO) Algorithm during searching target solution, the paper supplies a Self-Adapting Improvement PSO (SAI-PSO) Algorithm including of its improvement methods, strategies and operational structure from the angles of evaluation characteristic generated and fitness function selected. The experimental results show that SAI-PSO uses self-adapting strategies to amend inertia weighing and particles selection mechanism, it could reduce the amount of computation for particles optimization, strength the algorithm convergence speed, obtains the better solution precision, which provides the valuable lessons and references for Multi-Criteria Group Decision-making resolution.