Abstract
This paper proposes a new hybrid metaheuristic algorithm called teaching-learning artificial bee colony (TLABC) for function optimization. TLABC combines the exploitation of teaching learning based optimization (TLBO) with the exploration of artificial bee colony (ABC) effectively, by employing three hybrid search phases, namely teaching-based employed bee phase, learning-based onlooker bee phase, and generalized oppositional scout bee phase. The performance of TLABC is evaluated on 30 complex benchmark functions from CEC2014, and experimental results show that TLABC exhibits better results compared with previous TLBO and ABC algorithms.
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Acknowledgements
This work was supported in part by Natural Science Foundation of Jiangsu Province (Grant No. BK 20160540), China Postdoctoral Science Foundation (Grant No. 2016M591783), and National Natural Science Foundation of China (Grant No. 61703268).
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Chen, X., Xu, B. (2018). Teaching-Learning-Based Artificial Bee Colony. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_17
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DOI: https://doi.org/10.1007/978-3-319-93815-8_17
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