Abstract
Although showing competitive performances in many real-world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. Addressing these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) has been developed in the literature. This paper describes the adoption of Fuzzy Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) for software module clustering problem. Comparative studies with the original Teaching Learning based Optimization (TLBO) and other Fuzzy TLBO variant demonstrate that ATLBO gives superior performance owing to its adaptive selection of search operators based on the need of the current search.
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Acknowledgements
This work is supported by the Fundamental Research Grant from Ministry of Higher Education Malaysia (MOHE) under the title “A Reinforcement Learning Sine Cosine based Strategy for Combinatorial Test Suite Generation (grant no: RDU170103).” We are grateful to MOHE for this support.
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Zamli, K.Z., Din, F., Ramli, N., Ahmed, B.S. (2019). Software Module Clustering Based on the Fuzzy Adaptive Teaching Learning Based Optimization Algorithm. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_3
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DOI: https://doi.org/10.1007/978-981-13-6031-2_3
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