@Article{2018.100000017,
AUTHOR = {Yasir Mehmood, Waseem Shahzad},
TITLE = {An Accelerated Convergent Particle Swarm Optimizer (ACPSO) of Multimodal Functions},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {1},
PAGES = {91--103},
URL = {http://www.techscience.com/iasc/v25n1/39635},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2018.100000017}
}