Particle Swarm Optimization Using Velocity Control

Nakagawa, Naofumi; Ishigame, Atsushi; Yasuda, Keiichiro

doi:10.1541/ieejeiss.129.1331

Cited by 3 publications

(1 citation statement)

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“…and max_dist is the maximum distance of a particle from the global best in the previous generation. In recent research, the PSO algorithm with inertia weight adjusted by the average absolute value of velocity or the situation of swarm is proposed to keep the balance between local search and global search [56][57][58]. In addition, the adaptive population size strategy is an effective way to improve the accuracy and efficiency of the PSO algorithm [59][60][61][62].…”

Section: Adaptive Inertia Weight Strategiesmentioning

confidence: 99%

Effects of Random Values for Particle Swarm Optimization Algorithm

2018

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Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 (U[0, 1], U[−1, 1] and G(0, 1)), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U[−1, 1] or G(0, 1) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U[−1, 1] or G(0, 1) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

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