SMBO: A self-organizing model of marriage in honey-bee optimization

Authors:
Arit Thammano;Patcharawadee Poolsamran
Affiliations:
Computational Intelligence Laboratory, Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand;Computational Intelligence Laboratory, Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

This paper proposes a novel swarm intelligence technique, which is an adaptation of Abbass's marriage in honey-bee optimization (MBO), with the aim to achieve better overall performance than the original version of the MBO while also lowering the computation time for finding the optimal solution. The original MBO has been proven to be one of the best swarm intelligence algorithms for solving optimization problems. However, many parameters need to be properly set in order for the MBO to perform at its best. Therefore, long computation time caused by a large number of trial and error iterations involved in trying to find the right combination of parameters is unavoidable. The framework of the proposed algorithm is similar to the original MBO, which is based on the marriage behavior of honey-bees. In order to improve the efficiency of the MBO algorithm, several aspects of the original MBO have been adapted, such as (1) the proposed algorithm is adapted to obtain the ability to automatically search for the proper number of queens, (2) the proposed algorithm divides the problem space into several colonies, each of which has its own queen. In order to keep the number of colonies to a minimum, the proposed algorithm, therefore, encourages the queens to compete with each other for a larger colony and also urges the newly-born brood which is fitter than the queen of the colony to overthrow the queen. (3) the fuzzy c-means algorithm is employed to assign the drones to the proper colonies. The proposed algorithm has been evaluated and compared to the original MBO algorithm. The experimental results on six benchmark problems demonstrate the potential of the proposed algorithm in offering an efficient and effective solution to the problem.