Dynamic Hunting Leadership optimization: Algorithm and applications

Bahman Ahmadi*, Juan S. Giraldo, Gerwin Hoogsteen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
94 Downloads (Pure)

Abstract

The Dynamic Hunting Leadership (DHL) algorithm is an innovative heuristic technique that draws inspiration from nature to find almost optimal solutions for various optimization problems. It consists of four variants, each highlighting distinct leadership strategies to guide the hunting process. The development of the algorithm was based on the realization that effective leadership during the hunting process can significantly improve its efficacy. The concept behind these methods is to dynamically modify the number of leaders, which can enhance the algorithm's performance. The stability of DHL variants in exploring the unknown area of the search space and exploitation phases is compared, and the advantages of exploration or exploration ability for the different variants of DHL are discussed. Moreover, the results are compared with more than twenty well-known algorithms. The efficacy of the proposed algorithms in discovering near-optimal solutions is tested across several real-world applications, and the outcomes demonstrate that the DHL algorithm outperforms other competing algorithms. The distinctiveness of the DHL algorithm is its ability to identify the global minimum on various benchmark problems and its superior performance in enhancing the objective value for the welded beam design problem and tension–compression spring problem, surpassing the values achieved by other algorithms. The algorithms’ performance is also tested on an optimal allocation problem of distributed generation (DG) and energy storage system (ESS) for balanced electrical distribution systems. The results show that all four variants of DHL obtained the global minimum for the problem. For the optimal control strategy problem for voltage regulators in three-phase unbalanced power systems, the DHL algorithm improved the objective value by 35.7% compared to the best results found by other algorithms. Based on the analysis and comparison of the best objective values and convergence behavior for all benchmarks and problems, the DHL method proves to be an effective and reliable optimization method.

Original languageEnglish
Article number102010
JournalJournal of computational science
Volume69
DOIs
Publication statusPublished - May 2023

Keywords

  • Metaheuristic optimization algorithm
  • Nature-inspired computing
  • Optimization
  • Power system planning
  • Swarm intelligence
  • UT-Hybrid-D

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