Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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This paper presents solution of optimal power distribution networks problem of large-sized power systems via a genetic algorithm of real type. The objective is to find out the best power distribution network reliability while simultaneously minimizing the system expansion costs. To save an important CPU time, the constraints are to be decomposing into active constraints and passives ones. The active constraints are the parameters which enter directly in the cost function and the passives constraints are affecting the cost function indirectly as soft limits. The proposed methodology is tested for real distribution systems with dimensions that are significantly larger than the ones frequently found in the literature. Simulation results show that by this method, an optimum solution can be given quickly. Analyses indicate that proposed method is effective for large-scale power systems. Further, the developed model is easily applicable to n objectives without increasing the conceptual complexity of the corresponding algorithm and can be useful for very large-scale power system.