Particle swarm optimization based on the concept of tabu search

This paper presents a new Particle Swarm Optimization based on the concept of Tabu Search (TS-PSO). In PSO, when a particle finds a local optimal solution, all of the particles gather around the one, and cannot escape from it. On the other hand, TS can escape from the local optimal solution by moving away from the best solution at the present. The proposed TS-PSO is the method for combining the excellence of both PSO and TS. In this method, particles are divided into two categories called swarm1 and swarm2. And they play the key roles of intensification and diversification respectively. Swarm1 playing roles of intensification searches the area around the best solution at the present, and swarm2 playing roles of diversification intends to avoid local optimal solutions and to find global optimal one. Then, the proposed method is validated through numerical simulations with several functions which are well known as optimization benchmark problems comparing to the conventional PSO methods.