NDLI: A modified marriage in honey-bee optimization for multiobjective optimization problems

Content Provider	IEEE Xplore Digital Library
Author	Poolsamran, P. Thammano, A.
Copyright Year	2012
Description	Author affiliation: Computational Intelligence Laboratory, Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand (Poolsamran, P.; Thammano, A.)
Abstract	This paper proposes a modified marriage in honey-bee optimization for solving multiobjective optimization problems. Unlike the original marriage in honey-bee optimization, the proposed algorithm divides the objective space into several colonies, each of which has its own queen. The fitness of each solution is based on 3 parameters: the size of the colony, the number of dominating solutions, and the number of dominated solutions. The nondominated solutions with highest fitness values are preferentially assigned to be the queens while the rest are assigned to be the drones. Next, all drones are assigned to the colony according to their distances from the queens of the colonies. In order to maximize a genetic variance in the population, the multiple mating is used. The multiple mating requires the queen to mate with drones from the other colonies. The proposed algorithm has been evaluated and compared to two state-of-the-art metaheuristic algorithms: the Pareto archived evolution strategy and the nondominated sorting genetic algorithm. The experimental results on 5 different ZDT benchmark functions illustrate that the proposed algorithm is able to converge to the true Pareto fronts and has better spread of solutions, as compared with the published results of the two state-of-the-art algorithms.
Starting Page	338
Ending Page	343
File Size	246315
Page Count	6
File Format	PDF
ISBN	9781467322768
e-ISBN	9781467322782
DOI	10.1109/IS.2012.6335239
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-06
Publisher Place	Bulgaria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Sociology Statistics Measurement Genetic algorithms Convergence Equations multiobjective optimization problem swarm intelligence marriage in honey-bee optimization
Content Type	Text
Resource Type	Article

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