Modified parallel particle swarm optimization for global optimization using Message Passing Interface

Abstract

PSO has emerged as a powerful heuristic techuique for determining the global optimal solution of nonlinear optimization problems. Like all other evolutionary algorithms (EAs) it is also population based method. However, due to the inherent nature of PSO, it is desirable to parallelize it so as to get a better performance. In this paper, three versions of parallel PSO are presented. They are encoded using the Message Passing Interface (MPI) and are used to solve 16 benchmark scalable test problems available in literature. From the numerical and graphical analysis it is concluded that parallelization helps in enhancing the performance of basic PSO.

Highly Efficient Inverse Design of Semiconductor Optical Amplifiers Based on Neural Network Improved Particle Swarm Optimization Algorithm

“…For the inverse design of extracting the non-linear parameters of SOA, the parallel algorithm PSO can be used to find local and global optimal solutions [31], [32]. BPNN is used to optimise the PSO algorithm (BP-PSO) so that it iterates more rapidly while maintaining high accuracy.…”

Um Algoritmo Híbrido Paralelo Cooperativo Baseado em DE, PSO e AG: Uma Avaliação em Computadores Multicore

“…al. [9] usa uma nuvem de partículas (PSO) paralelas em algumas funções de benchmarks, melhorando a qualidade das soluções. Outros trabalhos nessa linha são: [10], [11] , [12] e [13].…”