A Modified Moth Swarm Algorithm Based on an Arithmetic Crossover for Constrained Optimization and Optimal Power Flow Problems

Abstract

The moth swarm algorithm (MSA) is a new meta-heuristic optimization technique inspired by the navigational style of moths in nature. This paper represents a novel modified MSA with an arithmetic crossover (MSA-AC) with the aim of improving the search for a global optimum, the convergence speed to an optimal solution, and the performance of the traditional MSA. The proposed MSA-AC method was applied in 23 standard benchmark test functions and used in six CEC 2005 composite benchmark test functions. Furthermore, in order to verify the success of the optimal solution, the MSA-AC approach was used to solve the optimal power flow problem in the two-terminal high-voltage direct current systems of the modified New England 39-bus and the modified WSCC 9-bus test systems. The numerical results obtained from the MSA-AC were compared with both the traditional MSA method and with various optimization algorithms presented in the literature. The outcomes obtained from the comparative results indicate the potential of the proposed approach in finding the global optimum and the convergence to an optimal solution.