融合改进Sine混沌映射的新型粒子群优化算法

In order to address the problems of uneven initial positions, ease of reaching local optimum, and low search accuracy in traditional particle swarm algorithm(PSO), a novel PSO algorithm based on an improved Sine chaotic mapping is proposed. The improved Sine chaotic mapping technique is used instead of the traditional pseudo-random number method for generating the initial particle population to enrich the population diversity. Two new position update mechanisms are added to the original basic position update formula. A Gaussian mutation operator is introduced to achieve a dynamic balance between the exploration and exploitation performance of the algorithm, as well as to help particles effectively jump out of the local optima during the iteration process. For three classical engineering optimization design problems with constraints, simulation experiments are performed for the proposed algorithm based on a benchmark test function consisting of seven single-peaked functions, six multi-peaked functions and ten fixed-dimensional functions. This algorithm is then compared with several other popular PSO variants. Simulation results show that the novel PSO algorithm based on improved Sine chaotic mapping has faster convergence speed and higher optimization-seeking accuracy than those of other PSO variants. For the benchmark test functions, it ranked first in 20 of them, accounting for about 87% of the total testing functions. The proposed algorithm ranked first in the overall performance of pressure vessel and I-beam design optimization, and can be used to solve some practical engineering optimization problems.