The bee colony optimization algorithm and its convergence

被引：14

作者：

Kruger, Tatjana Jaksic ^{[1
]}

Davidovic, Tatjana ^{[1
]}

Teodorovic, Dusan ^{[2
]}

Selmic, Milica ^{[2
]}

机构：

[1] Serbian Acad Arts & Sci, Math Inst, POB 367,Kneza Mihaila 36, Belgrade 11001, Serbia

[2] Univ Belgrade, Fac Transport & Traff Engn, Vojvode Stepe 305, Belgrade, Serbia

来源：

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION | 2016年 / 8卷 / 05期

关键词：

bio-inspired algorithms; swarm intelligence; foraging of honeybees; the BCO algorithm; optimisation problems; global optimum; meta-heuristic methods; stochastic processes; theoretical analysis; convergence properties; SEARCH; NETWORKS; PROOF;

D O I：

10.1504/IJBIC.2016.10000424

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The bee colony optimization (BCO) algorithm is a nature-inspired meta-heuristic method for dealing with hard, real-life combinatorial and continuous optimisation problems. It is based on the foraging habits of honeybees and was proposed by Lucic and Teodorovic in 2001. BCO is a simple, but effective meta-heuristic method that has already been successfully applied to various combinatorial optimisation problems in transport, location analysis, scheduling and some other fields. This paper provides theoretical verification of the BCO algorithm by proving some convergence properties. As a result, the gap between successful practice and missing theory is reduced.

引用

页码：340 / 354

页数：15

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