Improved Particle Swarm Algorithm Using Multiple Strategies

In order to address the issues of premature convergence and low search efficiency in the basic particle swarm algorithm, this paper analyzes the improved particle swarm optimization algorithms proposed by previous researchers. Based on this analysis, a modified algorithm for particle swarm optimization is proposed. The enhanced algorithm adjusts the inertia weight parameter in a nonlinear manner and dynamically changes the self-learning factor using chaos, allowing it to continuously change during iterations and achieve better optimization results. Additionally, to prevent the algorithm from getting trapped in local optima, a mutation operation is introduced into the improved algorithm. Finally, several classical test functions are used to conduct experiments, and the results are compared with the improved particle swarm optimization algorithms described in related literature. The experimental results demonstrate that the newly proposed algorithm achieves a certain degree of improvement in optimization accuracy compared to the basic particle swarm algorithm. This indicates that the modified algorithm effectively avoids premature convergence and possesses high search efficiency.