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Impact of Grey wolf optimization and modify Grey Wolf optimization on power flow have been presented 17, 18, and 19. Moth swarm algorithm has been presented for optimum power flow 20, and 21.
Effect of DG and capacitor; Hybrid configuration of the weight improved particle swarm optimization (WIPSO) algorithm and gravitational search algorithm (GSA) called the hybrid WIPSO–GSA algorithm has been used for obtained the optimal DG and capacitor location and size considering the reduction of the total apparent power loss 22. Modified version of the teaching–learning-based optimization technique has been used to optimize the optimal allocation of DG and capacitor considering minimization active power loss and maximizing network reliability 23. Genetic algorithm and Artificial Bee colony algorithm is used to obtained the optimal placement of capacitor and DG considering total power losses minimization 24. Genetic moth swarm algorithm (GMSA) is hybrid approach based on the genetic algorithm (GA) and moth swarm algorithm (MSA) used for reduction the electrical power loss by integrating DG and capacitor 25. New optimization algorithms, named intersect mutation differential evolution (IMDE) is presented to determine the optimally locate and the size of DGs and capacitors in distribution networks considering minimization of the power loss 26.
In this paper, the feasibility of WOA technique for the DG and capacitor optimal allocation problem is evaluated and its performance is compared with other algorithms that considering reduction of network power losses, voltage deviation, and total operating cost.
This paper is organized as follows: Section II present the formulation of the problem includes objective functions and constrains. The optimization algorithm is described in section III. The cases study and simulation results are described in section IV. Finally, the paper concludes in Section V.

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