Computational Optimization and Applications
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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The network reconfiguration optimal control in distribution networks is modeled as a multiobjective combinational optimization. Multiple objectives are considered for load balancing among the feeders, minimum deviation of the nodes voltage, minimize the power loss and branch current constraint violation. Based on the objectives evaluated by membership functions respectively, These objectives are modeled with fuzzy sets to evaluate their imprecise nature and one can provide the anticipated value of each objective. We propose a new Fuzzy Preferences Multi-Objective Approach to solve it. Simulation results demonstrated that the proposed method is effective in improving performance.