PSO-MEA Hybrid Optimization Algorithm and Its Convergence Analysis

In view of the disadvantages of low convergence precision and slow convergence rate of the single optimization algorithm, this study combined the mind evolutionary algorithm (MEA) and the particle swarm optimization (PSO) and proposed the PSO-MEA hybrid optimization algorithm. The similar-taxis operation of the mind evolutionary algorithm used the particle swarm optimization algorithm to search the optimal solution of the sub population. The sub populations in the whole population used the dissimilation operation of the mind evolutionary algorithm to find the global optimal solution. And the global convergence of hybrid optimization algorithm is used to proof the convergence of the algorithm. Finally, through the simulation analysis of three commonly used test functions, the PSO-MEA hybrid optimization algorithm has obvious improvement in the performance of the algorithm and has the characteristics of high convergence precision and fast convergence speed.