An Inertia Weight Variable Particle Swarm Optimization Algorithm with Mutation

To improve the precision, accuracy, and speed of the particle swarm optimization (PSO) algorithm, the influence of the function concavity of the inertia weight reduction strategy on the performance of the algorithm is analyzed by heuristic and contrast experiments. The advantages and disadvantages of different mutation strategies are analyzed, and a PSO algorithm is proposed based on the summary of the experimental results. The algorithm introduces a new type of inertia weight change strategy and mutation mode, so that the particles have different mutation probability in different periods of the algorithm, and the inertia weight is linearly reduced in segments by increasing the absolute value of the slope. The experimental results show that the proposed algorithm has better performance than the original algorithm in most of the selected test functions. In other words, the improvements to the PSO algorithm can improve the precision, speed, and correct rate of PSO algorithm.