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

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

doi:10.1109/ROMA.2014.7295881

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 proceeding › Conference contribution

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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 language	English
Title of host publication	2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	166-171
Number of pages	6
ISBN (Electronic)	978-1479957651
DOIs	https://doi.org/10.1109/ROMA.2014.7295881
Publication status	Published - 12 Oct 2015
Event	IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014 - Kuala Lumpur, Malaysia Duration: 15 Dec 2014 → 16 Dec 2014

Conference

Conference	IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014
Country/Territory	Malaysia
City	Kuala Lumpur
Period	15/12/14 → 16/12/14

Keywords

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

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10.1109/ROMA.2014.7295881

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Accepted author manuscript (Post-print), 900 KB

Cite this

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title = "Self-adaptive hybrid genetic algorithm using an ant-based algorithm",

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.",

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booktitle = "2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)",

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El-Mihoub, TA, Hopgood, A & Aref, IA 2015, Self-adaptive hybrid genetic algorithm using an ant-based algorithm. in 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)., 7295881, Institute of Electrical and Electronics Engineers Inc., pp. 166-171, IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014, Kuala Lumpur, Malaysia, 15/12/14. https://doi.org/10.1109/ROMA.2014.7295881

Self-adaptive hybrid genetic algorithm using an ant-based algorithm. / El-Mihoub, Tarek A.; Hopgood, Adrian; Aref, Ibrahim A.
2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). Institute of Electrical and Electronics Engineers Inc., 2015. p. 166-171 7295881.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

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Keywords

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