Multi-strategy improved gazelle optimization algorithm for numerical optimization and UAV path planning
Year: 2025
Type: article
Abstract: The Gazelle Optimization Algorithm (GOA) is a recently proposed and widely recognized metaheuristic algorithm. However, it suffers from slow convergence, low precision, and a tendency to fall into local optima when addressing practical problems. To address these limitations, we propose a Multi-Strategy Improved Gazelle Optimization Algorithm (MIGOA). Key enhancements include population initialization based on an optimal point set, a tangent flight search strategy, an adaptive step size factor, and novel exploration strategies. These improvements collectively enhance GOA's exploration capability, convergence speed, and precision, effectively preventing it from becoming trapped in local optima. We evaluated MIGOA using the CEC2017 and CEC2020 benchmark test sets, comparing it with GOA and eight other algorithms. The results, validated by the Wilcoxon rank-sum test and the Friedman mean rank test, demonstrate that MIGOA achieves average rankings of 1.80, 2.03, 2.03, and 2.70 on CEC2017 (Dim = 30/50/100) and CEC2020 (Dim = 20), respectively, outperforming the standard GOA and other high-performance optimizers. Furthermore, the application of MIGOA to three-dimensional unmanned aerial vehicle (UAV) path planning problems and 2 engineering optimization design problems further validates its potential in solving constrained optimization problems. Experimental results consistently indicate that MIGOA exhibits strong scalability and practical applicability.
Source: Scientific Reports
Authors: Lu Li, Haonan Zhao, Lixin Lyu, Fan Yang
Institutions: Anhui Business College, Northeastern University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4409698085