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Particle swarm optimization (PSO) is one of the most widely used intelligent algorithms, which hinders its further promotion because of its inherent shortcomings. This paper analyses the iterative characteristics and rules of particle swarm optimization, summarizes the causes and solutions of defects, and improves them one by one. From early to late stage, it improves particle swarm optimization algorithm and improves the performance of algorithm. The experimental results show that the comprehensive optimization model proposed in this paper has good applicability. The chaos particle swarm optimization algorithm based on cloud model is feasible in solving the multi-objective optimization problem. The results show that the comprehensive model algorithm proposed in this paper effectively solves the shortcomings of PSO algorithm, such as premature and easy to fall into local optimum, and the algorithm is effective and feasible for solving multi-objective reactive power optimization problems.

Research on Key Problems of Multi-objective Evolution Algorithm Based on Cloud Model and Chaotic Particle Swarm