A multiion particle swarm optimization algorithm based on repellent and attraction forces

Abstract

Summary Particle swarm optimization (PSO) is an iterative computational methods which is used for obtaining the solutions of practical optimization problems. PSO is however, prone to be ended up obtaining a local optimum. Various strategies are proposed in the related literature to address this issue. Such strategies, however, often reduce the convergence speed of the algorithms. This article proposes a multiion particle swarm optimization (MION‐PSO) algorithm which incorporates three strategies to balance the exploration and exploitation abilities of the algorithm. To improve the exploration abilities, the particles are regarded as ions with repellent/attraction forces among them. The second strategy is a multiion strategy (MIS) in which the population is divided into many subswarms, namely, particle group. Using MIS, the optimal solution within each group is then used to purposefully guide the updates of other individuals. To delete the useless particles without the capability of updating historical best location for a specific period and have a misleading impact on others, without we employ a particle elimination strategy. Twenty‐three benchmark functions and eight baseline algorithms are employed to test the performance of MION‐PSO method. The experimental results show that MION‐PSO method significantly outperforms the baseline algorithms in terms of convergence, and further exhibits high capability in finding the optimal solutions especially on unimodal functions.

An advanced hybrid algorithm for nonlinear function optimization with real world applications

“…To overcome such issues many different modifications of PSO proposed in the recent-past literature such as ELPSO, 35 DMeSR-PSO, 36 EPSO, 37 MLPSO, 38 PSOd, 39 DMSDL-PSO, 40 VPSO, 41 SHPSO, 42 PSOCO, 43 MTVPSO, 44 HAFPSO, 45 PSO+, 46 MPSO, 47 CMPSOWV, 48 NOPSO, 49 NMSPSO, 50 and MION-PSO. 51 Furthermore, hybrid strategy is one of the main research directions to improve performance of single algorithm. 52,53 Because of different optimization algorithms have different search behaviors and advantages.…”