Inbreeded GA for optimal distributed resource allocation in distribution systems

Abstract

Although the phenomenon of inbreeding is quite usual in animals, but it has not yet been attempted to enhance the performance of genetic algorithms (GAs). The crossover operator of GA can be modified using the concept of inbreeding in animals. This single measure enhances the pace of GA. However, GA stagnates due to inbreeding, as in animals, which is then overcome by suggesting mandatory mutation in addition to the conventional mutation. This study proposes inbreeded GA (IGA) which is then applied to efficiently solve the simultaneous optimal allocation of shunt capacitors and distributed generations in distribution systems while considering realities of practical distribution systems. The objective considered is to maximise annual energy loss reduction. The proposed method is applied on the benchmark IEEE 33-bus test distribution system. The application results obtained shows the supremacy of IGA over GA and other existing metaheuristics.