Abstract
The standard Bees Algorithms (SBA) is a population-based search algorithm inspired by the nature that revolves around mimicking the food foraging behavior of honey bees in order to solve optimization problems. This study had implemented the probabilistic method in Estimated Distribution Algorithm (EDA) into the SBA to improve the performance of the algorithm in terms of speed and accuracy. The newly proposed algorithm is tested on ten benchmark test functions. Then, the accuracy and speed are compared to SBA. The performance of the algorithm had also been validated on two engineering design optimization problems with specific constraints condition. The results of the benchmark test functions showed that the proposed algorithm provides very competitive results in terms of improved speed and convergence rate. The results of the design engineering optimization problems prove that the proposed algorithm can perform well in solving challenging problems with constrained and unknown search spaces.
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Acknowledgements
The authors gratefully acknowledge the financial support from UniMAPÂ and Ministry of Higher Education Malaysia under Fundamental Research Grant Scheme (FRGS) with grant No: FRGS 9003-00736.
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Bahari, M.S., Azmi, N.A., Yusof, Z.M., Pham, D.T. (2021). Bees Algorithm with Integration of Probabilistic Models for Global Optimization. In: Bahari, M.S., Harun, A., Zainal Abidin, Z., Hamidon, R., Zakaria, S. (eds) Intelligent Manufacturing and Mechatronics. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-0866-7_22
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DOI: https://doi.org/10.1007/978-981-16-0866-7_22
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