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

El-Mihoub, Tarek and Hopgood, Adrian and Aref, Ibrahim (2015) Self-adaptive hybrid genetic algorithm using an ant-based algorithm. In: UNSPECIFIED.

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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.

Item Type:
Contribution to Conference (Paper)
ID Code:
177523
Deposited By:
Deposited On:
25 Oct 2022 18:45
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 14:55