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		<title>Three Populations, Two Stages: New Evolutionary Algorithm Tackles Constrained Optimization</title>
		<link>https://bioengineer.org/three-populations-two-stages-new-evolutionary-algorithm-tackles-constrained-optimization/</link>
		
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		<pubDate>Sat, 10 Oct 2026 19:41:37 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[benchmark test suites]]></category>
		<category><![CDATA[Cluster Computing]]></category>
		<category><![CDATA[co-evolution]]></category>
		<category><![CDATA[constrained multi-objective optimization]]></category>
		<category><![CDATA[constraint handling]]></category>
		<category><![CDATA[diversity enhancement]]></category>
		<category><![CDATA[engineering optimization]]></category>
		<category><![CDATA[evolutionary algorithms]]></category>
		<category><![CDATA[Pareto front]]></category>
		<category><![CDATA[reference vectors]]></category>
		<category><![CDATA[stage switching]]></category>
		<category><![CDATA[Xi'an Polytechnic University]]></category>
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					<description><![CDATA[Researchers have developed CMOEA-TSD, a three-population, two-stage evolutionary algorithm that significantly outperforms seven state-of-the-art methods on constrained multi-objective optimization problems with sparse feasible regions.]]></description>
		
		
		
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