A Micro-Bacterial Foraging Algorithm for High-Dimensional Optimization

被引：21

作者：

Dasgupta, Sambarta ^{[1
]}

Biswas, Arijit ^{[1
]}

Das, Swagatam ^{[1
]}

Panigrahi, Bijaya Ketan ^{[2
]}

Abraham, Ajith ^{[3
,4
]}

机构：

[1] Jadavpur Univ, Dept Elect & Telecommun Engn, Kolkata, India

[2] Indian Inst Technol, Dept Elect Engn, Delhi, India

[3] Norwegian Univ Sci & Technol, Ctr Excellence Quantifiable Quality Serv, Norwegian, Norway

[4] MIR Labs, Auburn, AL USA

来源：

2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5 | 2009年

关键词：

DISTRIBUTED OPTIMIZATION; GLOBAL OPTIMIZATION; STABILITY ANALYSIS; BIOMIMICRY; SYNERGY; SWARMS; PSO;

D O I：

10.1109/CEC.2009.4983025

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Very recently bacterial foraging has emerged as a powerful technique for solving optimization problems. In this paper, we introduce a micro-bacterial foraging optimization algorithm, which evolves with a very small population compared to its classical version. In this modified bacterial foraging algorithm, the best bacterium is kept unaltered, whereas the other population members are reinitialized. This new small population mu-BFOA is tested over a number of numerical benchmark problem for high dimensions and we find this to outperform the normal bacterial foraging with a larger population as well as with a smaller population.

引用

页码：785 / +

页数：2

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