Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Abstract

This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm optimization semi-independently and compares it with the traditional particle swarm optimization. In addition, the comparison is extended to differential evolution and genetic algorithm. This presented comparative study provides a clear exposition of the effects introduced by the proposed algorithm. Performance of all evaluated optimizers is evaluated based on how well they perform in finding the global minima of 24 multi-dimensional benchmark functions, each having 7, 14, or 21 dimensions. Each algorithm is put through a session of self-tuning with 100 iterations to ensure convergence of their respective optimization parameters. The results confirm that the new variant is a significant improvement over the traditional algorithm. It also obtained notably better results than differential evolution when applied to problems with high-dimensional spaces relative to the number of available particles.

Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization

“…Single-objective algorithms based on PSO were proposed in [19,25]. Although there are quite few studies dealing with a singleobjective VNDPSO, e.g., [26][27][28][29], studies dealing with multi-objective VNDPSO-based algorithms are very rare. The so-called social class MOPSO algorithm (SCMOPSO) was introduced in [30] to solve the VND MO problem of wireless sensor network design.…”