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The movement of army soldiers during a war, in which each soldier actively moves near the optimal value, served as the inspiration for the swarm intelligence-based optimization method known as War Strategy Optimization (WSO). The WSO may not get away from sub-optimal traps, as it does not consider the actions of a bad soldier, who may spoil the winning probability of the war. This work thus attempts to develop a modified WSO (MWSO) to enhance the performance of the existing WSO by identifying the bad soldier and giving chance to mend his attitudes and behavior, or replacing him with a new soldier to escape from local optima. The developed MWSO has been studied on two distinct problems of the electric power system. The first test problem is the optimal design of a solar PV system with battery storage fed unified power quality conditioner (UPQC) supplying harmonic loads with variable irradiation with a goal of improving the power quality (PQ), while the other is the power system state estimation (SE). The superior performances of the MWSO were portrayed by comparing the results with those of bacterial foraging (BF) and genetic algorithms (GA).
Subramaniyan et al. (Tue,) studied this question.