A Multi-Strategy-Improved Northern Goshawk Optimization Algorithm for Global Optimization and Engineering Design  

作　　者：Liang Zeng Mai Hu Chenning Zhang Quan Yuan Shanshan Wang 

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第7期1677-1709,共33页计算机、材料和连续体（英文）

基　　金：supported by theKey Research and Development Project of Hubei Province(No.2023BAB094);the Key Project of Science and Technology Research Program of Hubei Educational Committee(No.D20211402);the Open Foundation of HubeiKey Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System(No.HBSEES202309).

摘　　要：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-distributionmutation 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.

关 键 词：Northern Goshawk Optimization tent chaotic map T-distribution disturbance state transition algorithm UAV path planning 

分 类 号：O17[理学—数学]