The Chaotic Particle Swarm Optimization Based on Sub-Energy Tunneling

The premature problem of Particle Swarm Optimization(PSO) leads the missing of the global optima and the failure of searching process. To solve this problem, the chaotic PSO based on sub-energy tunneling is proposed. This algorithm introduces the concept of sub-energy tunneling of TRUST algorithm to transform the objective function in optimization problem. And it uses the Logistic series to replace the random number in PSO. With maintaining the optimization properties of the original objective function, the speed of particles searching the optimization is accelerated. The difference and relationship between the sub-energy tunneling and traditional transform are analyzed. In the numerical experiment, the proposed algorithm and some PSOs based on function transform are compared. The results show that the PSO based on sub-energy tunneling has a higher convergence speed and it can improve the searching efficiency. So the chaotic PSO based on sub-energy tunneling is valid and effective.