Optimization of Optimal Power Flow Problem Using Multi-Objective Manta Ray Foraging Optimizer

Finding a feasible solution set for optimization problems in conflict with objective functions poses significant challenges. Moreover, in such problems, the level of complexity may increase depending on the geometry of the objective and decision spaces. The most effective methods in solving multi-objective problems having high levels of complexity are search algorithms using the Pareto-based archiving approach. Recently, the crowding distance approach has been used to improve the performance of the Pareto-based archiving method. This article presents research conducted on the development of a method that can find the optimum solution set for a multi-objective optimal power flow (MOOPF) problem whose objective functions are in conflict. For this purpose, a powerful and effective method was developed using the Pareto archiving approach based on crowding distance. The performance of the developed method was tested on twenty-four benchmark problems of different types and difficulty levels and compared with competing algorithms. The data obtained from the experimental trials and four different performance metrics were analyzed using statistical test methods. Analysis results showed that the proposed method yielded a competitive performance on different types of multi-objective optimization problems and was able to find the best solutions in the literature for the real-world MOOPF problem. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This article presents research on a method for solving multi-objective optimization problems with conflicting objective functions. The method, based on Pareto archiving and crowding distance, was tested on various benchmark problems and compared with other algorithms. The results demonstrate that the proposed method performs competitively in multi-objective optimization.