Multi-objective optimization of reactive power dispatch problem using fuzzy tuned mayfly algorithm

The recent multi -objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi -objective evolutionary techniques are population -based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi -objective power system problem. It has a selfadapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover ( P c ) and mutation ( P m ) probabilities. Moreover, a model of Pareto-dominance is employed to rank non -dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE -118 bus system and IEEE -24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non -dominating pareto-fronts as compared to other competing methods.