Parallel Optimization Based on Artificial Bee Colony Algorithm

被引：0

作者：

Li, Debo ^{[1
]}

Feng, Yongxin ^{[1
]}

Zhong, Jun ^{[1
]}

Zhou, Jielian ^{[1
]}

Yin, Libao ^{[1
]}

Zhou, Junhao ^{[1
]}

机构：

[1] Guangdong Power Grid Corp, Elect Power Res Inst, Guangzhou 510080, Guangdong, Peoples R China

来源：

2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA) | 2017年

关键词：

artificial bee colony algorithm; traveling salesman problem; parallel optimization; combinatorial optimization; swarm intelligence;

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 aims to tackle the shortcomings of the standard artificial bee colony algorithm (ABC) such as slow convergence, long solving time and being easy to fall into local optima. We study the state transformation formula and propose a parallelized ABC algorithm with Message Passing Interface (MPI). We use the traveling salesman problem (TSP) as the case study. Our experiments show that the parallel ABC algorithm has an advantage in speed over the standard algorithm w.r.t. iterations and convergence speed.

引用

页码：955 / 959

页数：5

共 12 条

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