[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83546-en":3,"doc-seo-83546-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83546,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","MMAO-Dyn: A Metabolic Multi-Agent Optimizer for Dynamic Optimization","This paper investigates whether the Metabolic Multi-Agent Optimizer (MMAO) can be credibly transformed into a dynamic-optimization method while preserving its core metabolic control loop. MMAO-Dyn redefines private energy, communal budget, role drift, success feedback, and lifecycle turnover under nonstationary environments where repeated changes invalidate prior local structure. Experiments on an 18-scenario synthetic benchmark matrix across 10D–30D evaluate MMAO-Dyn against generic and dynamic baselines and endogenous ablations.","MMAO-Dyn: A Metabolic Multi-Agent Optimizer  \nfor Dynamic Optimization  \nJinliang Xu∗ and Liping Ma  \narXiv :2607 .00846v2 [ cs .NE] 4 Jul 2026  \nAbstract—This paper studies whether the Metabolic MultiAgent Optimizer (MMAO) can be credibly derived into a dynamic-optimization method without replacing its core metabolic control loop by external adaptation modules. The proposed MMAO-Dyn maps private energy, communal budget, role drift, success feedback, and lifecycle turnover to a nonstationary setting in which environmental changes repeatedly invalidate previously useful local structure. We evaluate MMAODyn on an 18-scenario synthetic dynamic continuous benchmark matrix covering shifted sphere, shifted Ackley, and shifted Rastrigin landscapes at 10D, 20D, and 30D, with two changeseverities and 12 seeds per scenario. The comparison layer includes a generic MMAO variant without dynamic derivation, dynamic random search, dynamic PSO-lite, dynamic DE-lite, and three endogenous ablations. Across the full 216-run matrix, MMAO-Dyn attains mean offline error 28.07, improving over Generic-MMAO (29 .36), Dynamic-PSO-lite (34 .65), DynamicDE-lite (67 .09), and Dynamic-RandomSearch (111 .37). The gains are clearest in aggregate robustness on sphere and Rastrigin families and in 10-step post-change recovery relative to the generic backbone, whereas the seed-aligned comparison with Dynamic-PSO-lite remains unfavorable in win-loss count and the NoMemoryRefresh ablation stays very close to the full method. The strongest resulting claim is therefore derivational rather than dominance based: the metabolic loop can generate meaningful dynamic behavior, with its clearest current value lying in recovery-oriented resource redistribution.  \nIndex Terms—Dynamic optimization, adaptive metaheuristics, endogenous resource allocation, nonstationary search, MMAO.  \nI. INTRODUCTION  \nDYNAMIC optimization problems differ from static opti  \nmization not only because the target moves, but because useful search behavior must be redistributed over time. A method that performs well on a fixed landscape may still fail badly when changes invalidate prior local structure, alter promising regions, or reward faster recovery over slower asymptotic precision. For this reason, dynamic optimization is a natural stress test for any framework whose identity depends on endogenous allocation of search effort rather than on one fixed move operator [1]–[3] .  \nThe Metabolic Multi-Agent Optimizer (MMAO) is a crossdomain heuristic framework in which agents earn, spend, donate, and reinvest bounded resources through a privatepublic metabolic loop. The central hypothesis behind MMAOis not that one operator is universally best, but that useful search pressure can emerge from a closed resource economy  \nJinliang Xu is an independent researcher in Beijing, China; e-mail: jlxu[fly@gmail.com](fly@gmail.com).  \nLiping Ma is with the Department of Disease Control and Prevention, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China; e-mail: [lipingmaqzx@163.com](lipingmaqzx@163.com).  \namong heterogeneous agents. This makes dynamic optimization especially relevant: if the metabolic loop really is the organizing principle of the framework, it should remain meaningful after environmental changes, when the optimizer must redirect effort instead of merely refining a static incumbent.  \nThis paper asks whether the same metabolic logic can be derived into a credible optimizer for dynamic optimization without abandoning the core loop in favor of an externally attached change-handling module. The goal is therefore narrower than a full state-of-the-art challenge paper. We do not claim that a small synthetic study resolves the broader dynamic-optimization literature. Instead, we ask whether a recognizably derived MMAO family member can exhibit coherent post-change behavior, outperform a generic non-derived MMAO backbone, and produce interpretable evidence that the metabolic cont","cbCais72b8yLWgs5","https://ap.wps.com/l/cbCais72b8yLWgs5","pdf",353015,3,1,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What is the main research question of MMAO-Dyn?\",\"answer\":\"The paper asks whether MMAO’s metabolic control logic can be derived into a credible dynamic-optimization method without replacing the core loop with external change-handling modules.\"},{\"question\":\"How does MMAO-Dyn model dynamics in the optimizer?\",\"answer\":\"It maps private energy, communal budget, role drift, success feedback, and lifecycle turnover to a nonstationary setting where environmental changes repeatedly invalidate previously useful local structure.\"},{\"question\":\"What benchmark setup is used to evaluate MMAO-Dyn?\",\"answer\":\"The method is tested on an 18-scenario synthetic dynamic continuous benchmark matrix including shifted sphere, shifted Ackley, and shifted Rastrigin landscapes at 10D, 20D, and 30D, with two change severities and multiple seeds per scenario.\"}]",1784188734,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"mmao-dyn-a-metabolic-multi-agent-optimizer-for-dynamic-optimization","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":20},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/mmao-dyn-a-metabolic-multi-agent-optimizer-for-dynamic-optimization/83546/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main research question of MMAO-Dyn?","Question",{"text":74,"@type":75},"The paper asks whether MMAO’s metabolic control logic can be derived into a credible dynamic-optimization method without replacing the core loop with external change-handling modules.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does MMAO-Dyn model dynamics in the optimizer?",{"text":79,"@type":75},"It maps private energy, communal budget, role drift, success feedback, and lifecycle turnover to a nonstationary setting where environmental changes repeatedly invalidate previously useful local structure.",{"name":81,"@type":72,"acceptedAnswer":82},"What benchmark setup is used to evaluate MMAO-Dyn?",{"text":83,"@type":75},"The method is tested on an 18-scenario synthetic dynamic continuous benchmark matrix including shifted sphere, shifted Ackley, and shifted Rastrigin landscapes at 10D, 20D, and 30D, with two change severities and multiple seeds per 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