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An Accelerated Convergent Particle Swarm Optimizer (ACPSO) of Multimodal Functions

Yasir Mehmood, Waseem Shahzad

Department of Computer Science, National University of Computer and Emerging Science, Islamabad, Pakistan

* Corresponding Author: Yasir Mehmood, email

Intelligent Automation & Soft Computing 2019, 25(1), 91-103. https://doi.org/10.31209/2018.100000017

Abstract

Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms most of the compared PSO variants on a diverse set of problems.

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Cite This Article

Y. Mehmood and W. Shahzad, "An accelerated convergent particle swarm optimizer (acpso) of multimodal functions," Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 91–103, 2019.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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