Abstract
Particle Swarm Optimization (PSO) is the global optimization technique that inspires many researchers to solve large scale of non-linear optimization problems. For certain complex scenarios, the premature convergence problem of PSO algorithm cannot find global optimum in dynamic environments. In this paper, a new variant motility factor based Cellular Particle Swarm Optimization (m-CPSO) algorithm is proposed which is developed by the migration behavior observed from fibroblast cellular organism to overcome this problem. The proposed m-CPSO algorithm is modeled in two different social best and individual best models. The performance of m-CPSO is tested in the benchmark and real-time data instances and compared with classical PSO. The outcome of experimental results has demonstrated that m-CPSO algorithm produces promising results than classical PSO on all evaluated environments. Article DOI: https://dx.doi.org/10.20319/mijst.2017.32.209229 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
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