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Characteristic Analysis of Artificial Bee Colony Algorithm with Network-Structure
Author(s) -
Shunta Imamura,
Toshiya Kaihara,
Nobutada Fujii,
Daisuke Kokuryo,
Akira Kitamura
Publication year - 2017
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2017.p0496
Subject(s) - artificial bee colony algorithm , swarm intelligence , computer science , benchmark (surveying) , bees algorithm , artificial intelligence , foraging , swarm behaviour , algorithm , mathematical optimization , particle swarm optimization , metaheuristic , mathematics , ecology , geodesy , biology , geography
The artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bees, is one of the swarm intelligence systems. This algorithm can provide an efficient exploration of the optimal solutions using three different types of agents for optimization problems with multimodal functions. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm using the network structure of agents to enhance the ability for global search. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark functions.

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