Scuba Diver Optimization Algorithm for Constrained Car Side Impact Structural Design

Scuba Diver Optimization Algorithm for Constrained Car Side Impact Structural Design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Scuba Diver Optimization Algorithm for Constrained Car Side Impact Structural Design Saman M. Almufti, Amira Bibo Sallow This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9222545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Engineering design optimization problems characterized by highly nonlinear, tightly constrained, and mixed-variable formulations remain challenging for conventional metaheuristic approaches, particularly in maintaining feasibility while achieving competitive objective values. The car side impact design problem (CSIDP) is a well-established benchmark that exemplifies these difficulties due to its narrow feasible region and strong coupling between structural and safety constraints. This paper extends the Scuba Diver Optimization Algorithm (SDOA)—a recently introduced population-based metaheuristic inspired by the physiology and behaviour of scuba divers in open water—to the CSIDP. An adaptive depth-regulated framework is designed in which each candidate solution (diver) evolves under an oxygen-guided schedule that controls the transition between global exploration, moderate exploration, local exploitation, fine-tuning, and full reset. The proposed SDOA variant incorporates elite preservation, adaptive mutation, penalty-based constraint handling, and a hybrid local search tailored to the CSIDP. A MATLAB implementation is presented together with a modular flowchart that explicitly connects the algorithmic stages to diver physiology (oxygen decay, depth selection, ascent, and reset). The effectiveness of the proposed algorithm is validated on the CSIDP benchmark through extensive numerical experiments under multiple parameter configurations. The results demonstrate that the proposed SDOA consistently achieves high-quality feasible solutions with improved robustness and competitive objective values compared to several metaheuristic methods reported in the literature. Detailed constraint satisfaction analysis further approves that the obtained solutions lie close to crashworthiness critical safety boundaries, highlighting the engineering relevance of the proposed approach. These findings indicate that the proposed SDOA provides a reliable and effective optimization framework for complex constrained engineering design problems. Scuba Diver Optimization Algorithm car side impact design constrained structural optimization metaheuristics crashworthiness Full Text Additional Declarations No competing interests reported. Supplementary Files exprementalresultpart.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9222545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625623826,"identity":"d03c765d-1f93-48ed-8224-289409927d06","order_by":0,"name":"Saman M. 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The car side impact design problem (CSIDP) is a well-established benchmark that exemplifies these difficulties due to its narrow feasible region and strong coupling between structural and safety constraints.\u003c/p\u003e \u003cp\u003eThis paper extends the Scuba Diver Optimization Algorithm (SDOA)\u0026mdash;a recently introduced population-based metaheuristic inspired by the physiology and behaviour of scuba divers in open water\u0026mdash;to the CSIDP. An adaptive depth-regulated framework is designed in which each candidate solution (diver) evolves under an oxygen-guided schedule that controls the transition between global exploration, moderate exploration, local exploitation, fine-tuning, and full reset. The proposed SDOA variant incorporates elite preservation, adaptive mutation, penalty-based constraint handling, and a hybrid local search tailored to the CSIDP. A MATLAB implementation is presented together with a modular flowchart that explicitly connects the algorithmic stages to diver physiology (oxygen decay, depth selection, ascent, and reset).\u003c/p\u003e \u003cp\u003eThe effectiveness of the proposed algorithm is validated on the CSIDP benchmark through extensive numerical experiments under multiple parameter configurations. The results demonstrate that the proposed SDOA consistently achieves high-quality feasible solutions with improved robustness and competitive objective values compared to several metaheuristic methods reported in the literature. Detailed constraint satisfaction analysis further approves that the obtained solutions lie close to crashworthiness critical safety boundaries, highlighting the engineering relevance of the proposed approach. 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