An improved mayfly algorithm based optimal power flow solution for regulated electric power network

This paper presents an improved mayfly algorithm (IMA) for identifying the optimum control settings of optimal power flow problem in regulated electric power networks. IMA is the improved version of the mayfly algorithm (MA) by implementing simulated binary crossover and polynomial mutation instead of arithmetic crossover and normal distribution mutation operators in MA. The attributes of genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA) are taken into account in IMA. Single objective functions such as total fuel cost, total active power losses, total voltage variation, and voltage stability index (VSI) are used to assess the performance of the algorithms. The optimal solution of each objective function is evaluated by representing the test systems in MATPOWER. The results of IMA are compared with GA, PSO, and MA. Investigations based on the optimal solution, convergence characteristics, and statistical measures of the solution ensure IMA's superiority over alternative algorithms. The performance of the algorithms is evaluated by simulation of the IEEE-30 bus system, 62-bus Indian utility system and the IEEE-118 bus system. For IEEE-30 bus system the optimal solutions of the objective functions are 802.1448 $/hr, 3.6487 MW, 0.5279 pu and 0.1247. In case of 62-bus utility system the optimal solutions of the objective functions are 13305.4267 $/hr, 73.8746 MW, 0.8049 pu and 0.0986. For IEEE-118 bus system the optimal solutions of the objective functions are 129611.5389 $/hr, 76.5261 MW, 0.8632 pu and 0.0611 are obtained by implementing IMA.