Open Access

ARTICLE

Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

by Adnan Ashraf¹, Abdulwahab Ali Almazroi², Waqas Haider Bangyal^3,*, Mohammed A. Alqarni⁴

1 Govt. College Women University Sialkot, Sialkot, 51310, Pakistan
2 University of Jeddah, College of Computing and Information Technology at khulais, Dept. of information Technology, Jeddah, Saudi Arabia
3 University of Gujrat, Gujrat, 50700, Pakistan
4 University of Jeddah, College of Computer Science and Engineering, Dept. of Software Engineering, Jeddah, Saudi Arabia

* Corresponding Author: Waqas Haider Bangyal. Email:

Intelligent Automation & Soft Computing 2022, 31(1), 191-206. https://doi.org/10.32604/iasc.2022.015810

Received 08 December 2020; Accepted 24 May 2021; Issue published 03 September 2021

Abstract

Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population’s initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the torus sequence with PSO known as TO-PSO. The torus sequence belongs to the family of low discrepancy sequence and it is utilized in the proposed variant of PSO for the initialization of swarm. The proposed strategy of population’s initialization has been observed with the fifteen most famous unimodal and multimodal benchmark test problems. The outcomes of our proposed technique display outstanding performance as compared with the traditional PSO, PSO initialized with Sobol Sequence (SO-PSO) and Halton sequence (HO-PSO). The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the other classical approaches. Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.

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Citations