An Adaptive Mutation Strategy Dung Beetle Optimizer for Pattern Synthesis of Thinned Antenna Array

An Adaptive Mutation Strategy Dung Beetle Optimizer for Pattern Synthesis of Thinned Antenna Array | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 July 2025 V1 Latest version Share on An Adaptive Mutation Strategy Dung Beetle Optimizer for Pattern Synthesis of Thinned Antenna Array Authors : Ruiyou Li 0000-0002-2213-3352 [email protected] , Min Li , Chen Hu , Wen Liao , and Xinliang Li Authors Info & Affiliations https://doi.org/10.22541/au.175309987.77958154/v1 Published International Journal of Numerical Modelling: Electronic Networks, Devices and Fields Version of record Peer review timeline 161 views 123 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In addressing the pattern synthesis of multi-constraint thinned planar antenna arrays, existing intelligent optimization algorithms encounter limitations such as premature convergence and insufficient solution accuracy. Therefore, we propose an adaptive mutation strategy dung beetle optimizer (AMSDBO) for optimizing thinned planar antenna arrays. The AMSDBO algorithm innovatively constructs a three-stage collaborative optimization framework, effectively achieving high-performance design for thinned planar arrays under the constraints of fixed aperture size and sparsity rate through a parameter adaptive adjustment mechanism. Firstly, initial solutions for the dung beetle populations are generated using a chaotic mapping reverse learning joint strategy to enhance both population diversity and the quality of the initial solutions. Next, an adaptive T -distribution mechanism is imposed to effectively enhance the early global search capability. Finally, the Lévy flight variation strategy is employed to adaptively adjust the positions of the dung beetle population in the later stages, helping the algorithm avoid local optima and accelerating convergence. Simulation results with classical test functions demonstrate that AMSDBO offers significant advantages in convergence accuracy and robustness compared to traditional algorithms (DBO, PSO and IWO). Additionally, two sets of typical planar thinned array experimental results indicate that the optimization performance of the AMSDBO algorithm is significantly improved compared to traditional optimization algorithms. Specifically, there is a peak sidelobe level (PSLL) reduction of 15.5% for DBO, 11.64% for PSO, and 14.56% for IWO. This confirms the effectiveness and superiority of the AMSDBO algorithm. Supplementary Material File (an adaptive mutation strategy dung beetle optimizer for pattern synthesis of thinned antenna array.docx) Download 2.15 MB Information & Authors Information Version history V1 Version 1 21 July 2025 Peer review timeline Published International Journal of Numerical Modelling: Electronic Networks, Devices and Fields Version of Record 4 Feb 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords lévy flight chaotic map dung beetle optimizer peak sidelobe level thinned array Authors Affiliations Ruiyou Li 0000-0002-2213-3352 [email protected] Jiangxi University of Finance and Economics View all articles by this author Min Li Jiangxi University of Finance and Economics View all articles by this author Chen Hu State Grid Jiangxi Electric Power Co Ltd Power Supply Service Management Center View all articles by this author Wen Liao Jiangxi University of Finance and Economics View all articles by this author Xinliang Li Jiangxi University of Finance and Economics View all articles by this author Metrics & Citations Metrics Article Usage 161 views 123 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ruiyou Li, Min Li, Chen Hu, et al. An Adaptive Mutation Strategy Dung Beetle Optimizer for Pattern Synthesis of Thinned Antenna Array. Authorea . 21 July 2025. DOI: https://doi.org/10.22541/au.175309987.77958154/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175309987.77958154/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00e3da7be8109d6',t:'MTc3OTY0NjA1NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();