Fast artificial bee colony and its application to stereo correspondence |
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| Affiliation: | 1. Computer and Network Center, National Cheng Kung University, Tainan 701, Taiwan;2. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan;1. Institute of Humanities, Arts and Sciences, Federal University of Southern Bahia, BR-367, Km 10, CEP: 45810-000, Porto Seguro, Bahia, Brazil;2. Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo, Av. Trabalhador São-carlense, 400, Caixa Postal: 668, CEP: 13560-970, São Carlos, São Paulo, Brazil;3. Department of Computation and Mathematics, School of Philosophy, Science and Literature in Ribeirão Preto, University of São Paulo, Av. Bandeirantes, 3900, CEP: 14090-901, Ribeirão Preto, São Paulo, Brazil;1. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin 34185-1416, Iran;2. Singapore Institute of Technology, Singapore 13868, Singapore;3. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore;4. Singapore Institute of Manufacturing Technology, Singapore 638075, Singapore |
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| Abstract: | Nature-inspired meta-heuristics have gained popularity for solutions to many real-world complex problems, and the artificial bee colony algorithm is one of the most powerful optimisation methods among meta-heuristics. However, inefficient exploitation of onlooker bees prevents the artificial bee colony algorithm from finding the final result accurately and efficiently for complex problems. In this paper, a novel optimisation method is proposed based on the artificial bee colony algorithm. The proposed optimisation method adaptively exploits onlooker bees over generations. In addition, the proposed optimisation method is applied to a stereo-matching problem to minimise the segment-based integer energy function, which is also introduced in this paper. The experimental results show that the proposed optimisation method outperforms state-of-the-art population-based meta-heuristics, such as the genetic algorithm, differential evolution, conventional artificial bee colony, and clonal selection algorithm, for benchmark functions as well as for the stereo-matching problem. |
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