首页|求解工程优化问题的多种智能优化算法仿真

求解工程优化问题的多种智能优化算法仿真

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探究新型优化算法的寻优性能和工程优化问题求解能力，选取 6 种智能算法:天鹰优化器(AO)、算术优化算法(AOA)、非洲秃鹫优化算法(AVOA)、人工大猩猩部队优化器(GTO)、饥饿游戏搜索算法(HGS)、野马优化器(WHO)，对其进行仿真对比。首先阐述新型算法的主体框架;然后，选取6 个基准测试函数并测试其寻优性能;最后，用其求解 2 种典型的工程优化问题，并且分析其改进方向和应用前景。对于测试函数的仿真结果分析，GTO算法的寻优能力最强，多次获取测试函数理论值，且运行时间较短;在工程优化问题的仿真中，GTO算法与WHO算法的寻优能力较为突出，寻优时间短，可靠性高。
Simulation of Multiple Intelligent Optimization Algorithms for Solving Engineering Optimization Problems
To explore the optimization performance and engineering optimization problem-solving ability of the new optimization algorithms,six intelligent algorithms were selected:Aquila Optimizer(AO),Arithmetic Optimization Algorithm(AOA),African Vultures Optimization Algorithm(AVOA),Artificial Gorilla Troops Optimizer(GTO),Hunger Games Search(HGS),Wild Horse Optimizer(WHO),and simulated and compared.First,the main framework of the novel algorithms were expounded;Then,six benchmark functions were selected and tested;Finally,they were used to solve two typical engineering optimization problems,and analyze their improvement direction and application prospect.The analysis of the simulation results of the test function shows that,the GTO algorithm has the strongest op-timization ability,obtains the theoretical value of the test function for many times,and has a short running time;For solving engineering optimization problems,the GTO algorithm and WHO algorithm have outstanding optimization capa-bilities,short optimization time,and high reliability.In addition,the improved strategies and methods of six new opti-mization algorithms and their development prospects are analyzed.

Aquila optimizerArithmetic optimization algorithmAfrican vultures optimization algorithmArtificial gorilla troops optimizerHunger games searchWild horse optimizer

张金钱、王先鹏、孔凡康、曾勇

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中国航天科工集团贵州航天凯山石油仪器有限公司,贵州 贵阳 550025

天鹰优化器 算术优化算法 非洲秃鹫优化算法 人工大猩猩部队优化器 饥饿游戏搜索算法 野马优化器

国家重点研发计划贵州省科技计划贵州省科技计划

2020YFB1314100黔科合支撑[2021]345黔科中引地[2021]4013

2024

计算机仿真
中国航天科工集团公司第十七研究所

计算机仿真

CSTPCD
影响因子：0.518
ISSN：1006-9348
年,卷(期)：2024.41(5)