chaotic-based particle swarm optimization with inertia weight for optimization tasks

Among a variety of meta-heuristic population-based search algorithms, particle swarm optimization (pso) with adaptive inertia weight (aiw) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may be trapped in a local minimum through a limited number of iterations. to increase its diversity as well as enhance its exploration ability, this paper inserts a chaotic factor, generated by three chaotic systems, along with a perturbation stage into aiw-pso to avoid premature convergence, especially in complex non-linear problems. to assess the proposed method, a known optimization benchmark containing non-linear complex functions is selected and its results are compared with those of standard pso, aiw-pso, and genetic algorithm (ga). the empirical results demonstrate the superiority of the proposed chaotic aiw-pso to the counterparts over 21 functions, which confirms the promising role of inserting the randomness into aiw-pso. the behavior of error through the epochs show that the proposed manner can smoothly find proper minima in a timely manner without encountering a premature convergence.