Research on improved particle-swarm-optimization algorithm based on ant-colony-optimization algorithm

Abstract

In order to alleviate Linearly Decreasing Weight of Particle Swarm Optimization (LDW-PSO) algorithm falling into the local optimum, Particle Swarm Optimization combined with Ant Colony Optimization (PSO-ACO) algorithm is designed. A pseudo-random-proportional rule is introduced to the determination of the swarm optimum value in PSO for improving the swarm diversity. The calculation expression of particle positions is improved in combination with the calculation expression of the pheromone concentration, which makes particles pay more attention to the current search information and accelerate the search speed. The simulation experiment results show that PSO-ACO has higher convergence accuracy and satisfactory solution speed in the solution of several typical test-functions.