Self-adaptive differential evolution algorithm in constrained real-parameter optimization

被引：174

作者：

Brest, Janez ^{[1
]}

Zumer, Viljem ^{[1
]}

Maucec, Mirjam Sepesy ^{[1
]}

机构：

[1] Univ Maribor, Fac Elect Engn & Comp Sci, Smetanova Ul 17, SLO-2000 Maribor, Slovenia

来源：

2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6 | 2006年

关键词：

D O I：

10.1109/CEC.2006.1688311

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

Differential Evolution (DE) has been shown to be a powerful evolutionary algorithm for global optimization in many real problems. Self-adaptation has been found to be high beneficial for adjusting control parameters during evolutionary process, especially when done without any user interaction. In this paper we investigate a self-adaptive differential evolution algorithm where more DE strategies are used and control parameters F and CR are self-adapted. The performance of the self-adaptive differential evolution algorithm is evaluated on the set of 24 benchmark functions provided for the CEC2006 special session on constrained real parameter optimization.

引用

页码：215 / +

页数：2

共 23 条

[1]

Population set-based global optimization algorithms:: some modifications and numerical studies [J].