A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

Abstract

This paper proposes a modified honey badger algorithm (MHBA) for solving the optimal power flow (OPF) problem. This problem is a highly non-linear, non-convex and complex optimization problem with several decision variables and constraints. The original honey badger algorithm (HBA) has the problem of trapping in local optima due to the loss of population diversity, especially in solving complex optimization problems. Therefore, the MHBA aims at sufficient improvement in finding the optimal solution and feasibility. Opposition-based learning strategy (OBL) is integrated with the MHBA to preserve the diversity of the population and enhance the convergence toward the optimal solution. The effectiveness of the MHBA algorithm is evaluated on five objective functions of the OPF problem namely, total generation fuel cost minimization, active power and reactive power transmission losses minimization, voltage deviation and voltage stability enhancement. The performance of the proposed algorithm is tested and validated on the IEEE 30-bus test system. The proposed MHBA is compared with the HBA and other nature-inspired optimization algorithms reported in the literature. The results indicate that the proposed MHBA algorithm has the superiority to jump out of the local optimal and better convergence in solving the OPF problem. This is due to the strategy used in the algorithm which helps in maintaining the population diversity and provides a proper balance between exploration and exploitation.

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

“…The HBA and Adam algorithms were the next most effective in improving the predictive power of the DNN model. HBA has the advantages of a simple structure, few tuning parameters, easy implementation, powerful global search and efficient solution of the local optimization problem (Hashim et al, 2022;Yasear & Ghanimi, 2022). Adam has high computational efficiency, requires less memory and performs particularly well with large datasets, such as those used in the landslide susceptibility analysis in this study (Kingma & Ba, 2014;Salem et al, 2022).…”

Guided Pelican Algorithm

“…Rather than determined based on the fitness quality as in KMA, in SKA, the big male, small male, and female are determined randomly based on certain threshold [28]. In modified honey badger algorithm (MHBA), the leader is the best solution so far [29]. But each solution follows the leader based on two options.…”

“…But each solution follows the leader based on two options. The first option is randomized sinusoidal movement and the second option is randomized non-sinusoidal movement [29].…”

Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

“…In recent years, there are many studies proposing new swarm intelligence-based metaheuristics. Many metaheuristics were built based on the animal's behavior, such as emperor penguin colony (EPC) [4], red deer algorithm (RDA) [5], butterfly optimization algorithm (BOA) [6], Komodo mlipir algorithm (KMA) [7], stochastic Komodo algorithm (SKA) [8], squirrel search optimizer (SSO) [9], modified honey badger algorithm (MHBA) [10], northern goshawk optimizer (NGO) [11], guided pelican algorithm (GPA) [12], and so on. Some metaheuristics were built based on the human social activities, such as teaching learning-based optimizer (TLBO) [13], election-based optimization algorithm (EBOA) [14], driving training-based optimizer (DTBO) [15], modified social forces algorithm (MSF) [16], and so on.…”