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Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimiza...

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Published in:Scientific World Journal pp. 438260 - 438261
Main Authors: Chen, Tinggui, Xiao, Renbin
Format: research Journal Article
Published: Wiley-Blackwell 2014
Online Access:View this record in EBSCOhost
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      jtl: Scientific World Journal
      issn: 1537744X
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      dt: 2014
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2014/438260
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        PMC3948480
        103820464
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        atl: Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.
      aug:
        au:
          Chen, Tinggui
          Xiao, Renbin
        affil: College of Computer Science & Information Engineering, Zhejiang Gongshang University, Zhejiang Province, Hangzhou 310018, China.
      sug:
        subj:
          Algorithms
          Artificial Intelligence
          Models, Theoretical
      ab: Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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