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An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots
KTH, School of Computer Science and Communication (CSC).
2013 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 120, 509-517 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a Co-evolutionary Improved Genetic Algorithm (CIGA) for global path planning of multiple mobile robots, which employs a co-evolution mechanism together with an improved genetic algorithm (GA). This improved GA presents an effective and accurate fitness function, improves genetic operators of conventional genetic algorithms and proposes a new genetic modification operator. Moreover, the improved GA, compared with conventional GAs, is better at avoiding the problem of local optimum and has an accelerated convergence rate. The use of a co-evolution mechanism takes into full account the cooperation between populations, which avoids collision between mobile robots and is conductive for each mobile robot to obtain an optimal or near-optimal collision-free path. Simulations are carried out to demonstrate the efficiency of the improved GA and the effectiveness of CIGA.

Place, publisher, year, edition, pages
2013. Vol. 120, 509-517 p.
Keyword [en]
Genetic Algorithm (GA), Co-evolution, Co-evolutionary Improved Genetic Algorithm (CIGA), Global path planning, Multiple robots
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-133514DOI: 10.1016/j.neucom.2013.04.020ISI: 000324847100055Scopus ID: 2-s2.0-84882926369OAI: oai:DiVA.org:kth-133514DiVA: diva2:662278
Note

QC 20131106

Available from: 2013-11-06 Created: 2013-11-06 Last updated: 2017-12-06Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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More styles
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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