JADE: adaptive differential evolution with a small population

被引：24

作者：

Brown, Craig ^{[1
]}

Jin, Yaochu ^{[2
]}

Leach, Matthew ^{[3
]}

Hodgson, Martin ^{[1
]}

机构：

[1] Bosch Thermotechnol Ltd, Worcester WR4 9SW, Worcs, England

[2] Univ Surrey, Dept Comp, Guildford GU2 7XH, Surrey, England

[3] Univ Surrey, Fac Engn & Phys Sci, Ctr Environm Strategy, Guildford GU2 7XH, Surrey, England

来源：

SOFT COMPUTING | 2016年 / 20卷 / 10期

基金：

英国工程与自然科学研究理事会;

关键词：

Micro differential evolution; Small population; External archive; JADE; OPTIMIZATION; ALGORITHM; SIZE;

D O I：

10.1007/s00500-015-1746-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a new differential evolution (DE) algorithm for unconstrained continuous optimisation problems, termed JADE, that uses a small or 'micro' () population. The main contribution of the proposed DE is a new mutation operator, 'current-by-rand-to-pbest.' With a population size less than 10, JADE is able to solve some classical multimodal benchmark problems of 30 and 100 dimensions as reliably as some state-of-the-art DE algorithms using conventionally sized populations. The algorithm also compares favourably to other small population DE variants and classical DE.

引用

页码：4111 / 4120

页数：10

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