Hybrid Multiobjective evolutionary Algorithm Based Technique for Economic Emission Load Dispatch Optimization Problem | ||||
The International Conference on Electrical Engineering | ||||
Article 57, Volume 7, 7th International Conference on Electrical Engineering ICEENG 2010, May 2010, Page 1-12 PDF (164.7 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/iceeng.2010.33021 | ||||
View on SCiNiTO | ||||
Authors | ||||
A. A. Mousa; Kotb A. Kotb; Adel Y. Elmekawy | ||||
Department of Mathematics, Faculty of Sciences, El- Taif University, El- Taif, KSA. | ||||
Abstract | ||||
Abstract: In This paper, we present a hybrid approach combining two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The EELD problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. Our approach integrates the merits of both genetic algorithm (GA) and local search (LS). The proposed approach employs the concept of coevolution and repair algorithm for handling nonlinear constraints. Also, it maintains a finitesized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of -dominance. The use of -dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto set approximation. To improve the solution quality we implement local search (LS) technique as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test system. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem. | ||||
Keywords | ||||
Economic emission load dispatch; Evolutionary algorithms; Multiobjective optimization, Local search | ||||
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