Adaptive Crossover Operators in Memetic Algorithms for Solving the Capacitated Vehicle Routing Problem

Adaptive Crossover Operators in Memetic Algorithms for Solving the Capacitated Vehicle Routing Problem | 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 Adaptive Crossover Operators in Memetic Algorithms for Solving the Capacitated Vehicle Routing Problem Mohamed Salim Amri Sakhri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8196696/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 Optimizing delivery routes remains a fundamental challenge in logistics, where the Capacitated Vehicle Routing Problem (CVRP) serves as a key optimization framework. This paper introduces an enhanced memetic algorithm, building upon Vidal's Hybrid Genetic Search, that incorporates a novel family of Adaptive Crossover (AX) strategies. These strategies dynamically adjust recombination behavior based on real-time search performance and solution quality feedback. Extensive experiments on the standard CVRP benchmarks from Uchoa et al. (2017) demonstrate that the best AX configuration reduces the average optimality gap by 0.89 percentage points compared to the classical Order Crossover (OX), representing an 18.7% relative improvement. Our findings establish adaptive recombination as a powerful mechanism for enhancing both solution quality and convergence efficiency in vehicle routing optimization. Capacitated Vehicle Routing Problem Adaptive Crossover Memetic Algorithms Hybrid Genetic Search Metaheuristics Full Text Additional Declarations No competing interests reported. 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. 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