Abstract
In this paper, an improved Particle Swarm Optimization Algorithm (GCPSO) is proposed to solve the shortcomings of the existing Particle Swarm Optimization Algorithm (PSO) which has low convergence precision, slow convergence rate and is easy to fall into local optimum when performing high-dimensional optimization in the late iteration. First, the whole particle swarm of the algorithm was divided into three sub-groups, and different ranges of inertia weight ω are set for balances global search and local search in each sub-group, which improves the algorithm’s ability to explore. Then we add Gaussian perturbation with the greedy strategy to PSO to avoid the algorithm falling into local optimum and improve the convergence speed. And finally, the proposed algorithm is compared with Genetic Algorithm (GA), PSO and Grasshopper Optimization Algorithm (GOA) to analyse its performance and speed. Through experimental analysis, GCPSO has a significant improvement at convergence speed, convergence accuracy and stability.
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