Parallel genetic algorithm for multi knapsack problem

被引：0

作者：

Qi, T ^{[1
]}

Zhou, SJ ^{[1
]}

Chang, GJ ^{[1
]}

机构：

[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China

来源：

CCECE 2003: CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3, PROCEEDINGS: TOWARD A CARING AND HUMANE TECHNOLOGY | 2003年

关键词：

parallel genetic algorithm; cluster of workstations; multi-Knapsack-Problem;

D O I：

暂无

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 begins by introducing the basic mechanics of genetic algorithm and discussing different ways to parallelize algorithm. A parallel genetic algorithm (PGA) is presented over a cluster of workstations by using the PVM library, which is used to handle communications among processors. Using the presented algorithm, the well-known 0-1 multi-Knapsack-Problem is computed. Simulation results are presented to show how the performance of the PGA is affected by variations on the number of nodes, population size and migration interval. Results indicate that the performance of PGA on multi-knapsack problem is sound and robust.

引用

页码：1115 / 1118

页数：4

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