Effects of Random Values for Particle Swarm Optimization Algorithm
School of Mathematics and Statistics, Central South University, Changsha 410083, China
School of Mathematics and Statistics, Jishou University, Jishou 416000, China
State Key Laboratory of High Performance Complex Manufacturing, Changsha 410083, China
Authors to whom correspondence should be addressed.
Received: 11 December 2017 / Revised: 3 February 2018 / Accepted: 13 February 2018 / Published: 15 February 2018
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 (
), 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
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
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.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Dai, H.-P.; Chen, D.-D.; Zheng, Z.-S. Effects of Random Values for Particle Swarm Optimization Algorithm. Algorithms 2018, 11, 23.
Dai H-P, Chen D-D, Zheng Z-S. Effects of Random Values for Particle Swarm Optimization Algorithm. Algorithms. 2018; 11(2):23.
Dai, Hou-Ping; Chen, Dong-Dong; Zheng, Zhou-Shun. 2018. "Effects of Random Values for Particle Swarm Optimization Algorithm." Algorithms 11, no. 2: 23.
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