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This paper studies memetic algorithms—a hybrid evolutionary approach—for dynamic optimization problems. It proposes an adaptive hill climbing method as the local search component, integrating greedy crossover-based and steepest mutation-based hill climbing. To improve convergence, it adds adaptive dual mapping and triggered random immigrants. Experiments on dynamically generated problems compare the method with related evolutionary algorithms, showing strong efficiency in dynamic environments.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-memetic-algorithm-with-adaptive-hill-climbing-strategy-for-dynamic-optimization-problems-research/137811/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-memetic-algorithm-with-adaptive-hill-climbing-strategy-for-dynamic-optimization-problems-research/137811.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-21","2026-08-23",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why do dynamic optimization problems require special handling from evolutionary algorithms?","Question",{"text":112,"@type":113},"Dynamic optimization problems change over time, so the optimum shifts. Once traditional evolutionary algorithms converge, they cannot adapt quickly to these environmental changes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What local search strategy does the proposed memetic algorithm use?",{"text":117,"@type":113},"It introduces an adaptive hill climbing method that combines greedy crossover-based hill climbing and steepest mutation-based hill climbing in cooperative and competitive fashions.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the paper address the convergence problem in dynamic environments?",{"text":121,"@type":113},"It adds two diversity maintaining methods: adaptive dual mapping and triggered random immigrants, aiming to prevent loss of exploration and improve performance.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},137811,1787447951,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},4398048950312,"https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","Soft Comput (2009) 13(8-9): 763–780 DOI 10.1007/s00500-008-0347-3  \nA Memetic Algorithm with Adaptive Hill Climbing Strategy for Dynamic Optimization Problems  \nHongfeng Wang 1 , Dingwei Wang 1 , Shengxiang Yang2  \n1 School of Information Science and Engineering, Northeastern University, Shenyang 110004, P. R. China {hfwang, [dwwang](dwwang}@mail.neu.edu.cn)[}](dwwang}@mail.neu.edu.cn)[@mail.neu.edu.cn](dwwang}@mail.neu.edu.cn)  \n2 Department of Computer Science, University of Leicester, University Road, Leicester LE1 7RH, United Kingdom[s.yang@mcs.le.ac.uk](s.yang@mcs.le.ac.uk)  \nPublished online: 5 August 2008  \nAbstract Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the e􀀎ciency of the proposed memetic algorithm in dynamic environments.  \nKey words Genetic algorithm, memetic algorithm, local search, crossover-based hill climbing, mutation-based hill climbing, dual mapping, triggered random immigrants, dynamic optimization problems  \n1 Introduction  \nMany real-world optimization problems are dynamic optimization problems (DOPs), where the function landscapes may change over time and, thus, the optimum of these problems may also change over time. DOPs require powerful heuristics that account for the uncertainty present in the real world. Since evolutionary algorithms (EAs) draw their inspiration from the principles of natural evolution, which is a stochastic and dynamic  \nprocess, they also seem to be suitable for DOPs. However, traditional EAs face a serious challenge for DOPs because they cannot adapt well to the changing environment once converged.  \nIn order to address DOPs, many approaches have been developed [41] and can be grouped into four categories: 1) increasing population diversity after a change is detected, such as the adaptive mutation methods [4, 33]; 2) maintaining population diversity throughout the run, such as the immigrants approaches [11,39,40]; 3) memory approaches, including implicit [10,32] and explicit memory [2,35,38,43] methods; 4) multi-population [3,24] and speciation approaches [27] . A comprehensive survey on EAs applied to dynamic environments can be found in [14] .  \nIn recent years, there has been an increasing concern from the evolution computation community on a class of hybrid EAs, called memetic algorithms (MAs), which hybridize local search (LS) methods with EAsto re􀀌ne the solution quality. So far, MAs have been widely used for solving many optimization problems, such as scheduling problems [13,19,20], combinatorial optimization problems [8,30,31], multi-objective problems [9,12,18] and other applications [44,45] . However, these problems for which MAs have been applied are mainly stationary problems. MAs have rarely been applied for DOPs [6,7,36] . During the running course of general MAs, they may always exhibit very strong exploitation capacity due to executing e􀀎cient local re􀀌nement on individuals, but they may lose the exploration capacity as a result of the population converging to one optimum, which n","cbCaiaPgUxHAkdY8","https://ap.wps.com/l/cbCaiaPgUxHAkdY8","pdf",522950,17,"English","# Introduction\n## Dynamic optimization problems and evolutionary algorithms\n## Diversity maintenance and memory approaches\n## Motivation for memetic algorithms in dynamic environments\n# Investigated Algorithms\n## Framework of GA-based Memetic Algorithms\n# Experimental Results and Analysis\n# Conclusion and Future Work","[{\"question\":\"Why do dynamic optimization problems require special handling from evolutionary algorithms?\",\"answer\":\"Dynamic optimization problems change over time, so the optimum shifts. Once traditional evolutionary algorithms converge, they cannot adapt quickly to these environmental changes.\"},{\"question\":\"What local search strategy does the proposed memetic algorithm use?\",\"answer\":\"It introduces an adaptive hill climbing method that combines greedy crossover-based hill climbing and steepest mutation-based hill climbing in cooperative and competitive fashions.\"},{\"question\":\"How does the paper address the convergence problem in dynamic environments?\",\"answer\":\"It adds two diversity maintaining methods: adaptive dual mapping and triggered random immigrants, aiming to prevent loss of exploration and improve performance.\"}]","A Memetic Algorithm with Adaptive Hill Climbing Strategy for Dynamic Optimization Problems - Research | PDF",43]