Multi-Objective Distribution System Optimization using Euclidean Distance Calculations in the Genetic Operators

Andrea Mazza and Gianfranco Chicco


Distribution systems, multi-objective optimization, genetic operators, Euclidean distance, normalization


Recent developments in electrical distribution system optimization have introduced multi-objective problem formulations, using tools based on heuristics for their solutions. this paper considers two conflicting objectives (total losses and energy not supplied) and uses a genetic algorithm as the solution tool. The main original contribution of this paper is the computation of Euclidean distances inside the crossover function to select the best chromosome and drive the formation of the offsprings. two different types of computation of the Euclidean distance are presented. The proposed approach is applied to a case study carried out on the standard IEEE 14-node network.

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