A Multi-Strategy-Improved Northern Goshawk Optimization Algorithm for Global Optimization and Engineering Design
Liang Zeng1,2, Mai Hu1, Chenning Zhang1, Quan Yuan1, Shanshan Wang1,2,*
1 School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, China
2 Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, China
* Corresponding Author: Shanshan Wang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.049717
Received 16 January 2024; Accepted 11 April 2024; Published online 24 June 2024
Abstract
Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines. To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization (NGO) algorithm, particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes, this study introduces an advanced Improved Northern Goshawk Optimization (INGO) algorithm. This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency. Initially, a tent chaotic map is employed in the initialization phase to generate a diverse initial population, providing high-quality feasible solutions. Subsequently, after the first phase of the NGO’s iterative process, a whale fall strategy is introduced to prevent premature convergence into local optima. This is followed by the integration of T-distribution mutation strategies and the State Transition Algorithm (STA) after the second phase of the NGO, achieving a balanced synergy between the algorithm’s exploitation and exploration. This research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions, accompanied by a statistical analysis of the results. The experimental outcomes demonstrate INGO’s superior achievements in function optimization tasks. Furthermore, its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle (UAV) trajectory planning issues, establishing INGO’s capability in addressing complex optimization challenges.
Keywords
Northern Goshawk Optimization; tent chaotic map; T-distribution disturbance; state transition algorithm; UAV path planning
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