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

• Published in: Applied soft computing Vol. 116; p. 108334
• Main Authors: Kahraman, Hamdi Tolga, Akbel, Mustafa, Duman, Serhat
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2022
• Subjects: Crowd distance; Multi-objective improved manta ray foraging optimization; Multi-objective optimal power flow; Multi-objective optimization; Power system planning; Multi-objective optimal power flow; Multi-objective improved manta ray foraging optimization; Crowd distance; Multi-objective optimization; Power system planning
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.108334

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. •The IMOMRFO was developed using crowding distance-based Pareto archiving strategy.•The performance of the developed IMOMRFO was tested on the CEC 2020 benchmark suite.•The improvement in IMOMRFO performance was verified by statistical analysis.•The IMOMRFO was compared with the latest and strongest competitors.•The IMOMRFO was used for solving the multi-objective optimal power flow problem with different objective functions.