Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems

被引：13

作者：

Sallam, Karam M. ^{[1
]}

Elsayed, Saber M. ^{[1
]}

Sarker, Ruhul A. ^{[1
]}

Essam, Daryl L. ^{[1
]}

机构：

[1] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT, Australia

来源：

INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016 | 2017年 / 8卷

关键词：

D O I：

10.1007/978-3-319-49049-6_27

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem's landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.

引用

页码：371 / 387

页数：17

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