Impact of Problem Dimension on the Execution Time of Parallel Particle Swarm Optimization Implementation

被引：0

作者：

Altinoz, O. Tolga ^{[1
]}

Yilmaz, A. Egemen ^{[2
]}

Ciuprina, Gabriela ^{[3
]}

机构：

[1] TED Univ, Ankara, Turkey

[2] Ankara Univ, Ankara, Turkey

[3] Politeh Univ Bucharest, Bucharest, Romania

来源：

2013 8TH INTERNATIONAL SYMPOSIUM ON ADVANCED TOPICS IN ELECTRICAL ENGINEERING (ATEE) | 2013年

关键词：

particle swarm optimization; parallel computing; CUDA;

D O I：

10.1109/ATEE.2013.6563482

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this study, parallel particle swarm optimization algorithm has been investigated as regards the impact of the problem properties on the execution time. Two major factors affect the performance of parallel evolutionary algorithms: the population size and the problem dimension. In this study, five well-know benchmark functions have been applied with different dimensions. Then, these functions have been compared as regards the execution time. Finally, uniformly distributed population has been compared with the chaotic distributed population based on the dimension and population size from previous discussion.

引用

页数：6

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