[1]林梅金,汪震宇.改进收敛因子和变异策略的灰狼优化算法[J].佛山科学技术学院学报(自然科学版),2021,(03):001-6.
 LIN(Mei-jin,(WANG(Zhen-yu.Grey%wolf%optimization%algorithm%based8on%improved%convergence%factor%and%mutation%strategy[J].JOURNAL OF FOSHAN UNIVERSITY NATUAL SCIENCE EDITION,2021,(03):001-6.
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改进收敛因子和变异策略的灰狼优化算法
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《佛山科学技术学院学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年03期
页码:
001-6
栏目:
出版日期:
2021-05-15

文章信息/Info

Title:
Grey%wolf%optimization%algorithm%based8on%improved%convergence%factor%and%mutation%strategy
文章编号:
1008-0171(2021)03-0001-06
作者:
林梅金汪震宇
(佛山科学技术学院 机电工程与自动化学院,广东 佛山 528225)
Author(s):
LIN(Mei-jin(WANG(Zhen-yu
(School(of(Mechatronic(Engineering(and(Automation,(Foshan(University,(Foshan(528225,(China)
关键词:
灰狼优化算法反余弦函数变异策略测试函数寻优精度
Keywords:
grey( wolf( optimization( algorithm( arccosine( function( mutation( strategy( benchmark( functions(optimization(accuracy
分类号:
TP301.6
文献标志码:
A
摘要:
针对灰狼优化算法(GWO)在求解复杂优化问题时容易出现收敛速度慢和早熟收敛等缺点,提出了一种改进收敛因子和变异策略的新型灰狼优化算法(CMGWO)。为了平衡 GWO 算法的全局探索能力与局部开发能力,设计了一种基于反余弦函数变化策略的收敛因子;为了进一步提高算法跳出局部最优解的能力,提出了一种新的位置变异策略。 仿真实验结果表明,与已有的 3 种智能优化算法和 5 种典型改进灰狼优化算法相比,改进算法具有更快的收敛速度和更高的寻优精度,更适用于解决各种函数优化问题。关键词:灰狼优化算法;反余弦函数;变异策略;测试函数;寻优精度
Abstract:
Aiming+ at+the+ shortcomings+ of+ grey+wolf+ optimization+ algorithm+ (GWO)(when( dealing(with( complex(optimization( problems,( such( as( slow( convergence( and( premature( convergence,( an( improved( grey( wolf(optimization(algorithm(based(on(arccosine(convergence(factor(and(mutation(strategy( (CMGWO)(is(proposed.(In(order(to(keep(the(balance(between(exploration(and(exploitation,(inspired(by(arccosine(function,(a(new(arccosine(convergence( factor( is( proposed.( To( further( enhance( the( ability( to( jump( out( of( local( optimal( solution,( a( new(position(mutation(strategy(is(presented.(The( performance(of(CMGWO(is(investigated(on( solving( 16( benchmark(functions.( Compared( with( 3( intelligent( optimization( algorithms( and( 5( typical( improved( GWO( algorithms,( the(obtained( results( show( that( the( proposed( CMGWO( algorithm( has( faster( convergence( speed( and( higher(optimization(accuracy,(and(it(is(more(suitable(for(solving(various(function(optimization(problems.( grey( wolf( optimization( algorithm;( arccosine( function;( mutation( strategy;( benchm

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-12-06基金项目:广东省普通高校特色创新科研资助项目(2018KTSCX237,2019KZDZX1034)作者简介:林梅金(1981-),女,福建莆田人,佛山科学技术学院副教授,博士。
更新日期/Last Update: 2021-06-11