Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization

CATEGORY: ETHYLENE CRACKING
Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization

Type	Journal Article
Author	Kunjie Yu
Author	Xin Wang
URL	http://www.sciencedirect.com/science/article/pii/S095070511500502X
Volume	96
Pages	156-170
Publication	Knowledge-Based Systems
Date	March 15, 2016
Abstract	Authors present a novel variant of particle swarm optimization (PSO), namely, multiple learning PSO with space transformation perturbation (MLPSO-STP), to improve the performance of PSO. The MLPSO-STP employs an innovative learning strategy and STP. The strategy allows each particle to learn from the average information on the personal historical best position (pbest) of all particles and from the information on multiple best positions that are randomly chosen from the top 100p% of pbest. The strategy enables the preservation of swarm diversity to prevent premature convergence. STP increases the chance to find optimal solutions. The performance of MLPSO-STP is comprehensively evaluated in 21 unimodal and multimodal benchmark functions with or without rotation. Compared with eight popular PSO variants and seven state-of-the-art metaheuristic search algorithms, MLPSO-STP performs more competitively on the majority of the benchmark functions.