Self-adaptive hybrid genetic algorithm using an ant-based algorithm

Tarek A. El-Mihoub, Adrian Hopgood, Ibrahim A. Aref

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    143 Downloads (Pure)

    Abstract

    The pheromone trail metaphor is a simple and effective way to accumulate the experience of the past solutions in solving discrete optimization problems. Ant-based optimization algorithms have been successfully employed to solve hard optimization problems. The problem of achieving an optimal utilization of a hybrid genetic algorithm search time is actually a problem of finding its optimal set of control parameters. In this paper, a novel form of hybridization between an ant-based algorithm and a genetic-local hybrid algorithm is proposed. An ant colony optimization algorithm is used to monitor the behavior of a genetic-local hybrid algorithm and dynamically adjust its control parameters to optimize the exploitation-exploration balance according to the fitness landscape.

    Original languageEnglish
    Title of host publication2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages166-171
    Number of pages6
    ISBN (Electronic)978-1479957651
    DOIs
    Publication statusPublished - 12 Oct 2015
    EventIEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014 - Kuala Lumpur, Malaysia
    Duration: 15 Dec 201416 Dec 2014

    Conference

    ConferenceIEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014
    Country/TerritoryMalaysia
    CityKuala Lumpur
    Period15/12/1416/12/14

    Keywords

    • Ant Colony Optimization
    • Genetic Algorithms
    • Hybrid Genetic Algorithms
    • Memetic Algorithms
    • Self-adaptation

    Fingerprint

    Dive into the research topics of 'Self-adaptive hybrid genetic algorithm using an ant-based algorithm'. Together they form a unique fingerprint.

    Cite this