Conscious Neighborhood-Based Jellyfish Search Optimizer for Solving Optimal Power Flow Problems

Source abstract

Optimal Power Flow (OPF) problems are essential in power system planning, but their nonlinear and large-scale nature makes them difficult to solve with traditional optimization methods. Metaheuristic algorithms have become increasingly popular for solving OPF problems due to their ability to handle complex search spaces and multiple objectives. The Jellyfish Search Optimizer (JSO) is a metaheuristic algorithm that performs well for solving various optimization problems. However, it suffers from low exploration and an imbalance between exploration and exploitation. Therefore, this study introduces an improved JSO called Conscious Neighborhood-based JSO (CNJSO) to address these shortcomings. The proposed CNJSO suggests a new movement strategy named Best archive and Non-neighborhood-based Global Search (BNGS) to enhance the exploration ability. In addition, CNJSO adapts the concept of conscious neighborhood and the Wandering Around Search (WAS) strategy. The proposed CNJSO facilitates exploration of the search space and strikes a suitable balance between exploration and exploitation. The performance of CNJSO was evaluated on CEC 2018 benchmark functions, and the results were compared with those of ten state-of-the-art metaheuristic algorithms. In addition, the results were statistically validated using the Wilcoxon rank-sum and Friedman tests. Additionally, the effectiveness of CNJSO was assessed through the resolution of OPF problems. The experimental and statistical results confirm that the proposed CNJSO algorithm is competitive and superior to the compared algorithms.