Combining concepts of inertia weights and constriction factors in particle swarm optimization
Chuen-Yau Chen, Cheng-Hsueh Chuang, Meng-Cian Wu · 2012
A particle swarm optimization algorithm with global star topology designed by combining the concepts of the inertia weight and constriction factor is proposed in this paper. We enhance the global search ability at the beginning, while slowing down in the local search when the particles are near the local minimum by the linearly decreasing inertia weight. We apply the constriction factor with a value of 0.729 scales down the velocity step sizes, such that the particles can move without large overshoots at the beginning and smoothly approach the goals with a series of small steps when the particles are near the area of the optimal solution prior to the end of iterations. For a a quick convergence, the global star topology is chosen in this algorithm. The simulations performed on 2 well-known benchmark functions for over 50 runs indicate that the proposed algorithm with a population size of only 20 particles can achieve the goals quickly and accurately.