Buffered local search for efficient memetic agent-based continuous optimization

被引：7

作者：

Korczynski, Wojciech ^{[1
]}

Byrski, Aleksander ^{[1
]}

Kisiel-Dorohinicki, Marek ^{[1
]}

机构：

[1] AGH Univ Sci & Technol, Dept Comp Sci, Fac Comp Sci Elect & Telecommun, Al Mickiewicza 30, PL-30059 Krakow, Poland

来源：

JOURNAL OF COMPUTATIONAL SCIENCE | 2017年 / 20卷

关键词：

Memetic algorithms; Agent-based computing; Continuous optimization; Meta-heuristics; EVOLUTIONARY; ALGORITHMS; MODEL;

D O I：

10.1016/j.jocs.2017.02.001

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, a memetic search in classic and agent-based evolutionary algorithms are discussed. A local search is applied in an innovative way; namely, during an agent's life and in a classic way during the course of reproduction. Moreover, in order to efficiently utilize the computing power available, an efficient mechanism based on caching parts of the fitness function in the local search is proposed. The experimental results obtained for selected high-dimensional benchmark functions (with 5000 dimensions) show the apparent advantage of the proposed mechanism. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：112 / 117

页数：6

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