A NEW HYBRID EVOLUTIONARY OPTIMIZATION ALGORITHM FOR DISTRIBUTION FEEDER RECONFIGURATION

This article extends a hybrid evolutionary algorithm to cope with the feeder reconfiguration problem in distribution networks. The proposed method combines the Self-Adaptive Modified Particle Swarm Optimization (SAMPSO) with Modified Shuffled Frog Leaping Algorithm (MSFLA) to proceed toward the global solution. As with other population-based algorithms, PSO has parameters which should be tuned to have a suitable performance. Thus, a self-adaptive framework is proposed to adjust the parameters dynamically. In SAMPSO, the PSO learning factors are considered to be the new control variables and are changed in the evolutionary process. To enhance the quality of the solutions, the SAMPSO is combined with MSFLA and a new hybrid algorithm is proposed to minimize the electrical energy losses of the distribution system by feeder reconfiguration. The effectiveness of the proposed method is demonstrated through two test systems.