Parallelized linear time variant acceleration coefficients of PSO algorithm for global optimization

Abstract

The best time variant acceleration coefficients (TVAC) of Particle Swarm Optimization (PSO) algorithm is considered a difficult process. In addition to that issue, solution of complicated optimization problems using PSO makes PSO suffer slower performance. In this paper, a proposed algorithm called Parallelized Linear TVAC of PSO “PLTV-PSO” is implemented and evaluated. The proposed algorithm “PLTV-PSO” exploits the benefits the parallelization efficiency of PSO algorithm and effectiveness of linearly determining TVAC c 1 and c 2 of PSO. The main objective of “PLTV-PSO” is enhancing the global optimization search in the early part of the optimization and encouraging the particles to converge toward the global optima at the end of the search. The evaluation of “PLTV-PSO” is performed through empirical simulations by applying twelve well-known benchmark functions in “PLTV-PSO” and seven other optimization algorithms. The experimental results prove that PLTV-PSO provides better and efficient results than other optimization algorithms.