ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction...
| Publicado en: | Scientific World Journal Vol. 2015; pp. 392345 - 392346 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
1/1/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109733666&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109733666 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 1/1/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109733666 NLM25954768 2012997967 10.1155/2015/392345 NLM25954768 PMC4397471 109733666 ppf: 392345 ppct: 1 formats: tig: atl: ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization. aug: au: Sagban, Rafid Ku-Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani sug: ab: A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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