Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

Abd Allah A. Galal, Abd Allah A. Mousa, Bekheet N. Al-Matrafi

Department of Mathematics and Statistics, Faculty of Sciences, Taif University, Taif, KSA.

DOI: 10.4236/am.2013.44084                

Abstract

In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.

Keywords

Ant Colony; Genetic Algorithm; Fuzzy Numbers; Optimal Power Flow

Conflicts of Interest

The authors declare no conflicts of interest.

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