Genuine Power Loss Lessening by Monk Preaching inspired Optimization Algorithm

In this paper Monk preaching inspired optimization (MPO) algorithm is designed to solve the real power loss dwindling problem. Vivacious aims of the paper are Power constancy augmentation, power unconventionality minimization and loss lessening. MPO algorithm is designed by imitating the preaching of Monk to the pupil in the Hindu religion. Each pupil will acquire the knowledge in the preaching segment. Each pupil in the cluster will possess their own strength and weakness. Exclusive pupils own robust quick learning capability from the preaching's of Monk, whereas normal pupils have common learning skills. In this work learning impetus stratagem, opposition-based learning and resurrect stratagem are utilized to augment the comprehensive performance of the algorithm. MPO algorithm splits pupils into exclusive and normal pupils. Monk preaching method will be different with reference to the calibre of the pupils. Yet, every pupil is close to the Monk in spirit, consequently the procedure has improved exploitation capability. Every pupil will combine the substances of the Monk preaching about various deeds. Exclusive pupil possesses high calibre of learning from the preaching of the Monk while normal pupil will learn in medium or slow mode from the Monk. Validity of the Monk preaching inspired optimization (MPO) algorithm is confirmed by verifying in IEEE systems.