Particle Swarm Optimization Algorithm Mixed with Wolf Pack Algorithm

Abstract

 Since particle swarm optimization (PSO) is easy to enter the defect of local optimality, the global optimal solution is not easy to obtain, and the accuracy of the obtained global optimal solution is difficult to meet the standard;  The wolf pack algorithm has a relatively high accuracy of the optimal solution and the probability of searching for the optimal solution is also relatively large.  Therefore, this paper proposes a new algorithm SVRPSO that combines the wolf pack algorithm and the particle swarm optimization algorithm, which combines the advantages of the wolf pack algorithm and the particle swarm optimization algorithm.  Compared with the particle swarm optimization (PSO) algorithm, the SVRPSO algorithm has certain advantages, which can prevent the optimal value update stagnation in the later stage of the PSO algorithm, improve the probability of finding the optimal value, and the optimal value is closer to the theoretical value.  Experimental results show that the advantages of the SVRPSO algorithm over the PSO algorithm are that the optimal solution found is closer to the theoretical value and the convergence rate is faster.