Open Access
ARTICLE
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,
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.
Keywords
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. https://doi.org/10.31209/2018.100000017