Volume 17, Issue 3 (8-2020)                   2020, 17(3): 119-133 | Back to browse issues page

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Falahiazar A, Sharifi A, Seydi V. Providing a Multi-Objective Optimization Algorithm Based on Probabilistic Crossover and Bi-Directional Mutation for Solving I-Beam Designing Problem. Journal of Operational Research and Its Applications. 2020; 17 (3) :119-133
URL: http://jamlu.liau.ac.ir/article-1-1645-en.html
Assistant Professor, Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract:   (866 Views)
Today, multi-objective optimization algorithms are used as powerful tools for solving several problems. In any multi-objective optimization algorithm, diversity and convergence are two of the most important factors that need to be improved. Diversity and convergence are functions of exploration, exploitation, and selection operators. Effective algorithms should be used by different operators to achieve a robust optimization algorithm. In this study, a multi-objective optimization algorithm is proposed to enhance the diversity and convergence for solving the I-beam engineering problem. The proposed algorithm uses a proposed bi-directional mutation algorithm to exploit search space and uses the proposed probabilistic crossover algorithm to explore the search space. In this study, the hyper volume metric has been used to evaluate convergence and diversity. In the final section of this study, the overall performance of the proposed algorithm is compared with algorithms such as SPEA, NSGAII, NSPSO, and AWPSO in order to solve the I-beam designing problem. The results obtained from multi-objective optimization algorithms for solving I-beam designing problem indicate the superiority of the proposed algorithm in comparison to other known algorithms.
Full-Text [PDF 806 kb]   (270 Downloads)    
Type of Study: Research | Subject: Special
Received: 2018/02/25 | Accepted: 2019/07/14

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