02282nas a2200229   4500000000100000000000100001008004100002260001200043653003100055653003100086653001900117653002600136100001700162700002500179700002000204245009800224856009600322300001200418490000600430520160200436022001402038       2018                            d  c06/201810aRadial Distribution System10aOptimal Capacitor Location10aLoss Reduction10aMoth Swarm Algorithm.1 aEmad Mohamed1 aAl-Attar Ali Mohamed1 aYasunori Mitani00aMSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/05/ijimai_5_1_14_pdf_75537.pdf  a107-1220 v53 aThis work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors Lévy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms.  a1989-1660