Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness
- 1 Department of Mathematics and Statistics, Faculty of Sciences, Taif University, Taif, KSA
- 2 Department of Mathematics and Statistics, Faculty of Sciences, Taif University, Taif, KSA
- 3 Department of Mathematics and Statistics, Faculty of Sciences, Taif University, Taif, KSA
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 insta bilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy represen tation of the optimal power flow problem has been defined, where the input data involve many parameters whose possi ble 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 uncertain ties associated with efficiency data . The results on the standard IEEE systems demonstrate the capabilities of the pro posed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.
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