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Trends in Applied Sciences Research
  Year: 2014 | Volume: 9 | Issue: 5 | Page No.: 238-245
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Dynamic Parameters Optimization for Enhancing Performance and Stability of PSO

Amir Khosravani-Rad, Ramin Ayanzadeh and Elaheh Raisi

In this study, a new method for optimal control of parameters in particle swarm optimization based on fuzzy rules, is presented. In proposed method, to prevent premature convergence, social and personal learning coefficients are updated according to the convergence rate of the algorithm. In other words, fuzzy linguistic variables and membership functions are employed to conduct the swarm toward global optimum point. Several computational simulations are carried out to demonstrate high performance and stability of this method. Simulation results reveal superior optimality and stability and lower computational cost of the new algorithm compared to the traditional metaheuristics such as standard particle swarm optimization, genetic algorithms and standard particle swarm optimization which justifies its advantages for particle swarm optimization algorithms.
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How to cite this article:

Amir Khosravani-Rad, Ramin Ayanzadeh and Elaheh Raisi, 2014. Dynamic Parameters Optimization for Enhancing Performance and Stability of PSO. Trends in Applied Sciences Research, 9: 238-245.




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