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<html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta charset="utf-8"> <meta name="generator" content="pdf2htmlEX"> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> <link rel="stylesheet" href="https://static.pudn.com/base/css/base.min.css"> <link rel="stylesheet" href="https://static.pudn.com/base/css/fancy.min.css"> <link rel="stylesheet" href="https://static.pudn.com/prod/directory_preview_static/6260ba0b090cbf2c4ee31483/raw.css"> <script src="https://static.pudn.com/base/js/compatibility.min.js"></script> <script src="https://static.pudn.com/base/js/pdf2htmlEX.min.js"></script> <script> try{ pdf2htmlEX.defaultViewer = new pdf2htmlEX.Viewer({}); }catch(e){} </script> <title></title> </head> <body> <div id="sidebar" style="display: none"> <div id="outline"> </div> </div> <div id="pf1" class="pf w0 h0" data-page-no="1"><div class="pc pc1 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="https://static.pudn.com/prod/directory_preview_static/6260ba0b090cbf2c4ee31483/bg1.jpg"><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">A survey of dynamic</div><div class="t m0 x2 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">multiobjective optimization</div><div class="t m0 x3 h3 y3 ff2 fs1 fc0 sc0 ls0 ws0">Member： DeZheng Zhang</div><div class="t m0 x4 h4 y4 ff2 fs1 fc0 sc0 ls0 ws0">YunZhi Tian</div><div class="t m0 x5 h4 y5 ff2 fs1 fc0 sc0 ls0 ws0">Cong Pan</div><div class="t m0 x6 h3 y6 ff2 fs1 fc0 sc0 ls0 ws0">Date： November 28th,2020</div></div><div class="pi" data-data='{"ctm":[1.611850,0.000000,0.000000,1.611850,0.000000,0.000000]}'></div></div> </body> </html>

<div id="pf2" class="pf w0 h0" data-page-no="2"><div class="pc pc2 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="https://static.pudn.com/prod/directory_preview_static/6260ba0b090cbf2c4ee31483/bg2.jpg"><div class="t m0 x7 h5 y7 ff4 fs2 fc0 sc0 ls0 ws0">A survey of dynamic multiobjective optimization</div><div class="t m0 x8 h6 y8 ff5 fs3 fc0 sc0 ls0 ws0">A survey of dynamic multiobjective optimization</div><div class="t m0 x9 h4 y9 ff5 fs1 fc0 sc0 ls0 ws0">[Abstract]</div><div class="t m0 x9 h4 ya ff5 fs1 fc0 sc0 ls0 ws0">Dynamic multiobjective optimization problems (DMOPs) usually</div><div class="t m0 x9 h4 yb ff5 fs1 fc0 sc0 ls0 ws0">involve multiple conﬂicting objectives that change over time[1]. A</div><div class="t m0 x9 h4 yc ff5 fs1 fc0 sc0 ls0 ws0">good evolutionary algorithm should be able to quickly track the</div><div class="t m0 x9 h4 yd ff5 fs1 fc0 sc0 ls0 ws0">moving Pareto front (PF) and Pareto set (PS) over time.How to</div><div class="t m0 x9 h4 ye ff5 fs1 fc0 sc0 ls0 ws0">track the PF and PS eﬀectively is the most important problem</div><div class="t m0 x9 h4 yf ff5 fs1 fc0 sc0 ls0 ws0">in this ﬁeld.In order to solve the problem,many approaches have</div><div class="t m0 x9 h4 y10 ff5 fs1 fc0 sc0 ls0 ws0">been proposed and some of them get pretty results.</div><div class="t m0 x9 h4 y11 ff5 fs1 fc0 sc0 ls0 ws0">In this article, we aim to introduce several state-of-the-art ap-</div><div class="t m0 x9 h4 y12 ff5 fs1 fc0 sc0 ls0 ws0">proaches and some algorithms of them.</div><div class="t m0 x9 h4 y13 ff5 fs1 fc0 sc0 ls0 ws0">[Keywords]</div><div class="t m0 x9 h7 y14 ff5 fs1 fc0 sc0 ls0 ws0">dynamic；multiobjective；Pareto</div></div><div class="pi" data-data='{"ctm":[1.611850,0.000000,0.000000,1.611850,0.000000,0.000000]}'></div></div>