Two improved differential evolution schemes for faster global search

被引：266

作者：

Das, Swagatam ^{[1
]}

Konar, Amit ^{[1
]}

Chakraborty, Uday K. ^{[1
]}

机构：

[1] Jadavpur Univ, Elect & Telecom Eng Dept, Kolkata 700032, W Bengal, India

来源：

GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2 | 2005年

关键词：

algorithms; differential evolution; particle swarm optimization; evolutionary computation;

D O I：

10.1145/1068009.1068177

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. In this paper we present two new, improved variants of DE. Performance comparisons of the two proposed methods are provided against (a) the original DE, (b) the canonical particle swarm optimization (PSO), and (c) two PSO-variants. The new DE-variants are shown to be statistically significantly better on a seven-function test bed for the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.

引用

页码：991 / 998

页数：8

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