Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

Abstract

The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem.

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

“…At the same time, the experiment result reveals that the WOA benefitted from effectual exploitation capability, while the balance between search mechanisms and its exploration are insufficient to manage complicated realtime challenges, particularly in the OPF problems. As a result, the study presents a hybridization of whale and moth flame optimization (WMFO) to resolve the OPF problems effectively [18]. Movement strategy, population partitioning strategy, greedy selection operator, and a randomized boundary handling were introduced in this algorithm.…”

Advances in Sparrow Search Algorithm: A Comprehensive Survey

“…This method ensures the ability to exploit while avoiding the local optimum. During the exploration phase, the subpopulation is adjusted by directing individuals in the exploitation population, which can increase exploration efficacy [ 7 ].…”

Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data

“…Metaheuristic algorithms are a subset of approximate algorithms that have been used for solving many NP-hard problems in different fields of science, such as engineering design [39][40][41][42][43][44][45][46][47][48][49][50], task scheduling [51][52][53], engineering prediction [54][55][56][57][58], and optimal power flow [59][60][61][62][63][64] problems. When tackling the FS problem, metaheuristic algorithms have shown outstanding results in prior studies [65][66][67][68].…”