求解工程优化问题的多种智能优化算法仿真
Simulation of Multiple Intelligent Optimization Algorithms for Solving Engineering Optimization Problems
张金钱 1王先鹏 1孔凡康 1曾勇1
作者信息
- 1. 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
- 折叠
摘要
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.
Abstract
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.
关键词
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Key words
Aquila optimizer/Arithmetic optimization algorithm/African vultures optimization algorithm/Artificial gorilla troops optimizer/Hunger games search/Wild horse optimizer引用本文复制引用
基金项目
国家重点研发计划(2020YFB1314100)
贵州省科技计划(黔科合支撑[2021]345)
贵州省科技计划(黔科中引地[2021]4013)
出版年
2024
NETL
NSTL
万方数据