CELLULAR ORGANISM BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR COMPLEX NON-LINEAR PROBLEMS
DOI:
https://doi.org/10.20319/mijst.2017.32.209229Keywords:
Cellular Organism, Computational Model, Moving Peak Benchmark Function, Particle Swarm Optimization (PSO), Optimization, Population StructureAbstract
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.
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