Parallel Global Best-Worst Particle Swarm Optimization Algorithm for solving optimization problems

Abstract

The range of applications of swarm optimization algorithms is very vast. However, high dimensions and more number of decision variables make these optimization problems more complex. Particle Swarm Optimization (PSO) is the most popular optimizer for performing such types of optimization. PSO is motivated from the movement and intelligence of swarms. However, the primary constraint with the PSO and other swarm algorithms is enormous computational time (CT) due to more number of decision variables in complex problem. The number of steps inside Swarm Intelligence Algorithms (SIAs) also increase the complexity of computation in the process of optimization. Many iterations of the procedure of SIA need more CT since these algorithms are iterative in nature. In this study, a new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm has been proposed so as to provide a fully version of parallel algorithm. GBWPSO algorithm is the combination of PSO and Jaya algorithm that provides a refined version of parallel algorithm having more parallelism. The proposed algorithm is executed on three different computational hardware with various combinations of population size and maximum number of iteration on five different standard benchmark functions. The evaluation is done on the basis of performance metrics such as speedup (S), real speedup (RS), maximum speedup (MS), efficiency (E), and scalability. The proposed parallel algorithm (P-GBWPSO) outperforms both parallel version of PSO and Jaya algorithm in terms of less CT and better optimal solution. Based on the results, we found that system 03 (S3) is best on proposed GBWPSO algorithm with an efficiency of 1.2518 compare with system 01 (S1) and system 02 (S2).