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Erschienen in:
23.02.2017
Ant colony optimization with different crossover schemes for global optimization
Erschienen in: Cluster Computing | Ausgabe 2/2017
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Abstract
Global optimization, especially large scale optimization problems arise as a very interesting field of research, because they appear in many real-world problems. Ant colony optimization is one of optimization techniques for these problems. In this paper, we improve the continuous ant colony optimization (ACO\(_\mathrm{R})\) with crossover operator. Three crossover methods are employed to generate some new probability density function set of ACO\(_\mathrm{R}\). The proposed algorithms are evaluated by using 21 benchmark functions whose dimensionality is 30–1000. The simulation results show that the proposed ACO\(_\mathrm{R}\) with different crossover operators significantly enhance the performance of ACO\(_\mathrm{R}\) for global optimization. In the case the dimensionality is 1000, the proposed algorithm also can efficiently solves them. Compared with state-of-art algorithms, the proposal is a very competitive optimization algorithm for global optimization problems.