https://en.wikipedia.org/w/index.php?action=history&feed=atom&title=Multi-objective_optimization Multi-objective optimization - Revision history 2025-06-16T14:41:42Z Revision history for this page on the wiki MediaWiki 1.45.0-wmf.5 https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1294910179&oldid=prev Ngs111: /* A posteriori methods */ Fixed grammar 2025-06-10T14:50:30Z <p><span class="autocomment">A posteriori methods: </span> Fixed grammar</p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Previous revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 14:50, 10 June 2025</td> </tr><tr> <td colspan="2" class="diff-lineno">Line 202:</td> <td colspan="2" class="diff-lineno">Line 202:</td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== A posteriori methods ==</div></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== A posteriori methods ==</div></td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A posteriori methods aim at producing all the Pareto optimal solutions or a representative subset of the Pareto optimal solutions. Most a posteriori methods fall into either one of the following three classes:</div></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>A posteriori methods aim at producing all the Pareto optimal solutions or a representative subset of the Pareto optimal solutions. Most a posteriori methods fall into either one of the following three classes:</div></td> </tr> <tr> <td class="diff-marker" data-marker="−"></td> <td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[Mathematical programming]]-based a posteriori methods where an algorithm is <del style="font-weight: bold; text-decoration: none;">repeated</del> <del style="font-weight: bold; text-decoration: none;">and</del> each run <del style="font-weight: bold; text-decoration: none;">of the algorithm produces</del> one Pareto optimal solution;</div></td> <td class="diff-marker" data-marker="+"></td> <td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[Mathematical programming]]-based a posteriori methods where an algorithm is <ins style="font-weight: bold; text-decoration: none;">run</ins> <ins style="font-weight: bold; text-decoration: none;">repeatedly,</ins> each run <ins style="font-weight: bold; text-decoration: none;">producing</ins> one Pareto optimal solution;</div></td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Evolutionary algorithm]]s where one run of the algorithm produces a set of Pareto optimal solutions;</div></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Evolutionary algorithm]]s where one run of the algorithm produces a set of Pareto optimal solutions;</div></td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Deep learning]] methods where a model is first trained on a subset of solutions and then queried to provide other solutions on the Pareto front.</div></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* [[Deep learning]] methods where a model is first trained on a subset of solutions and then queried to provide other solutions on the Pareto front.</div></td> </tr> </table> Ngs111 https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1293105376&oldid=prev Maxeto0910: /* Scalarizing */ period after sentence 2025-05-30T18:30:42Z <p><span class="autocomment">Scalarizing: </span> period after sentence</p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Previous revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:30, 30 May 2025</td> </tr><tr> <td colspan="2" class="diff-lineno">Line 140:</td> <td colspan="2" class="diff-lineno">Line 140:</td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Scalarizing ===</div></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Scalarizing ===</div></td> </tr> <tr> <td class="diff-marker" data-marker="−"></td> <td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[File:NonConvex.gif|thumb|Linear scalarization approach is an easy method used to solve multi-objective optimization problem. It consists in aggregating the different optimization functions in a single function. However, this method only allows to find the supported solutions of the problem (i.e. points on the convex hull of the objective set). This animation shows that when the outcome set is not convex, not all efficient solutions can be found]]</div></td> <td class="diff-marker" data-marker="+"></td> <td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[File:NonConvex.gif|thumb|Linear scalarization approach is an easy method used to solve multi-objective optimization problem. It consists in aggregating the different optimization functions in a single function. However, this method only allows to find the supported solutions of the problem (i.e. points on the convex hull of the objective set). This animation shows that when the outcome set is not convex, not all efficient solutions can be found<ins style="font-weight: bold; text-decoration: none;">.</ins>]]</div></td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td> </tr> <tr> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Scalarizing a multi-objective optimization problem is an a priori method, which means formulating a single-objective optimization problem such that optimal solutions to the single-objective optimization problem are Pareto optimal solutions to the multi-objective optimization problem.&lt;ref name="HwangMasud1979" /&gt; In addition, it is often required that every Pareto optimal solution can be reached with some parameters of the scalarization.&lt;ref name="HwangMasud1979" /&gt; With different parameters for the scalarization, different Pareto optimal solutions are produced. A general formulation for a scalarization of a multi-objective optimization problem is</div></td> <td class="diff-marker"></td> <td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Scalarizing a multi-objective optimization problem is an a priori method, which means formulating a single-objective optimization problem such that optimal solutions to the single-objective optimization problem are Pareto optimal solutions to the multi-objective optimization problem.&lt;ref name="HwangMasud1979" /&gt; In addition, it is often required that every Pareto optimal solution can be reached with some parameters of the scalarization.&lt;ref name="HwangMasud1979" /&gt; With different parameters for the scalarization, different Pareto optimal solutions are produced. A general formulation for a scalarization of a multi-objective optimization problem is</div></td> </tr> </table> Maxeto0910 https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280045957&oldid=prev User-duck: Cite CE to eliminate errors and warnings, some cosmetic. 2025-03-12T03:59:27Z <p>Cite CE to eliminate errors and warnings, some cosmetic.</p> <a href="//en.wikipedia.org/w/index.php?title=Multi-objective_optimization&amp;diff=1280045957&amp;oldid=1280044023">Show changes</a> User-duck https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280044023&oldid=prev MrOllie: Reverted 1 edit by Zyzc75 (talk): Rv duplication 2025-03-12T03:38:38Z <p>Reverted 1 edit by <a href="/wiki/Special:Contributions/Zyzc75" title="Special:Contributions/Zyzc75">Zyzc75</a> (<a href="/wiki/User_talk:Zyzc75" title="User talk:Zyzc75">talk</a>): Rv duplication</p> <a href="//en.wikipedia.org/w/index.php?title=Multi-objective_optimization&amp;diff=1280044023&amp;oldid=1280043920">Show changes</a> MrOllie https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280043920&oldid=prev Zyzc75: /* No-preference methods */ 2025-03-12T03:37:33Z <p><span class="autocomment">No-preference methods</span></p> <a href="//en.wikipedia.org/w/index.php?title=Multi-objective_optimization&amp;diff=1280043920&amp;oldid=1280038730">Show changes</a> Zyzc75 https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280038730&oldid=prev MrOllie: Reverted 1 edit by Zyzc75 (talk) to last revision by Antured 2025-03-12T02:47:57Z <p>Reverted 1 edit by <a href="/wiki/Special:Contributions/Zyzc75" title="Special:Contributions/Zyzc75">Zyzc75</a> (<a href="/wiki/User_talk:Zyzc75" title="User talk:Zyzc75">talk</a>) to last revision by Antured</p> <p>Can't load revision 1280038730</p> MrOllie https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280038190&oldid=prev Zyzc75: /* Solution */ 2025-03-12T02:42:57Z <p><span class="autocomment">Solution</span></p> <p>Can't load revision 1280038190</p> Zyzc75 https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280037093&oldid=prev Antured: Restored revision 1280005389 by Moneytrees (talk): Possible copyright violation 2025-03-12T02:31:58Z <p>Restored revision 1280005389 by <a href="/wiki/Special:Contributions/Moneytrees" title="Special:Contributions/Moneytrees">Moneytrees</a> (<a href="/wiki/User_talk:Moneytrees" title="User talk:Moneytrees">talk</a>): Possible copyright violation</p> <p>Can't load revision 1280037093</p> Antured https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280031136&oldid=prev Zyzc75: /* Examples of applications */ 2025-03-12T01:32:40Z <p><span class="autocomment">Examples of applications</span></p> <p>Can't load revision 1280031136</p> Zyzc75 https://en.wikipedia.org/w/index.php?title=Multi-objective_optimization&diff=1280031019&oldid=prev Zyzc75: /* Introduction */ 2025-03-12T01:31:20Z <p><span class="autocomment">Introduction</span></p> <p>Can't load revision 1280031019</p> Zyzc75