Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization. Issue 3 (21st February 2016)

• Record Type: Journal Article
• Authors: Tejani, Ghanshyam G.; Savsani, Vimal J.; Patel, Vivek K.
• Is Part Of: Journal of computational design and engineering. Volume 3:Issue 3(2016)
• Page Start: 226
• Page End: 249
• Publication Date: 2016-02-21
• Subjects: Truss optimization -- Shape and size optimization -- Symbiotic organisms search (SOS) -- Metaheuristic; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1016/j.jcde.2016.02.003
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: The symbiotic organisms search (SOS) algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.