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Optimizing Composite Material Selection Using the Artificial Bee Colony Algorithm: A Comprehensive Approach
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Author(s): L. S. Sindhuja (Department of Networking and Mobile Application, PSG College of Arts & Science, Coimbatore, India), K. S. Shreenidhi (Department of Biotechnology, Rajalakshmi Engineering College, Chennai, India), Harishchander Anandaram (Department of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India), Saurabh Chandra (School of Law, Bennett University, Greater Noida, India)and B. S. Hari (Department of Mechanical Engineering, Kongu Engineering College, Erode, India)
Copyright: 2025
Pages: 26
Source title:
Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials
Source Author(s)/Editor(s): Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea), Bikash Ranjan Moharana (Papua New Guinea University of Technology, Papua New Guinea), Steve Korakan Ales (Papua New Guinea University of Technology, Papua New Guinea)and Dillip Kumar Biswal (Aryan Institute of Engineering and Technology, Bhubaneswar, India)
DOI: 10.4018/979-8-3693-7974-5.ch016
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Abstract
The selection of optimal materials for composite structures is an important step in engineering design, balancing performance, cost, and weight. In this chapter, the Artificial Bee Colony (ABC) algorithm, a nature-inspired optimization technique, is applied to efficiently identify the best composite material configurations. The ABC algorithm is inspired by the foraging behavior of honeybees, offering robust search capabilities and convergence properties. We discuss the principles underlying the ABC algorithm, its application to material selection, and performance evaluation criteria for composites. We compare it with traditional optimization techniques and make it clear that it outperforms them in solving complicated multidimensional problems. Case studies give an example of how it can be used for obtaining the optimal solution in several engineering applications. This chapter provides a comprehensive guide for researchers and engineers to improve design efficiency and performance by utilizing bio-inspired algorithms in material selection.
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