Abstract
Recently, Rao has proposed single-objective metaphor-less set of Rao algorithms to solve wide variety of optimization problems. The beauty of these algorithms lies in its simple implementation with no algorithm specific configuration of any parameters for the effective working of the algorithms. In this paper, we have attempted to extend these set of algorithms to multi-objective optimization using non-dominated sorting and crowding distance mechanisms. Subsequently, the proposed three variants, namely, Non-dominated Sorting Rao1 algorithm (abbreviated as NSRao1), Non-dominated Sorting Rao2 algorithm (abbreviated as NSRao2) and Non-dominated Sorting Rao3 algorithm (abbreviated as NSRao3) are successfully applied to solve multi-objective I-Beam optimization problem under various strength and geometric constraints. The three variants differ in the way the new solutions are formed from the best and worst solutions and their arbitrary interactions with other candidate solutions. NSRao1 technique was able to find better set of solutions towards minimizing the displacement area and where as it didn’t perform well towards the other boundary values compared to few standard evolutionary techniques. However, NSRao2 and NSRao3 are able to yield superior results in finding the optimal set of solutions towards both the boundary values with better diversity compared to other evolutionary algorithms.
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Kaur, J., Singh, P. (2023). Design of a Metaphor-Less Multi-objective Rao Algorithms Using Non-dominated Sorting and Its Application in I-Beam Design Optimization. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-driven Computing and Intelligent Systems. Lecture Notes in Networks and Systems, vol 653. Springer, Singapore. https://doi.org/10.1007/978-981-99-0981-0_63
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DOI: https://doi.org/10.1007/978-981-99-0981-0_63
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