Offline and Online Objective Reduction via Gaussian Mixture Model Clustering
The objective reduction has been regarded as a basic issue in many-objective optimization. Existing objective reduction methods identify one set of essential objectives using an approximate nondominated front. However, if the Pareto front (PF) of a many-objective optimization problem (MaOP) is irreg...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 2023-04, Vol.27 (2), p.341-354 |
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| container_title | IEEE transactions on evolutionary computation |
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| creator | Li, Genghui Wang, Zhenkun Zhang, Qingfu Sun, Jianyong |
| description | The objective reduction has been regarded as a basic issue in many-objective optimization. Existing objective reduction methods identify one set of essential objectives using an approximate nondominated front. However, if the Pareto front (PF) of a many-objective optimization problem (MaOP) is irregular, one single set of essential objectives may not be efficient for objective reduction. This article proposes to produce several different sets of essential objectives in objective reduction. More specifically, we use the Gaussian mixture model clustering to classify the obtained nondominated front into different subsets and perform objective reduction on each subset. Both an offline objective reduction method and an online objective reduction method are developed. The experimental results indicate that our proposed methods work well for MaOPs with degenerate or nondegenerate PFs. |
| doi_str_mv | 10.1109/TEVC.2022.3168836 |
| format | Article |
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Existing objective reduction methods identify one set of essential objectives using an approximate nondominated front. However, if the Pareto front (PF) of a many-objective optimization problem (MaOP) is irregular, one single set of essential objectives may not be efficient for objective reduction. This article proposes to produce several different sets of essential objectives in objective reduction. More specifically, we use the Gaussian mixture model clustering to classify the obtained nondominated front into different subsets and perform objective reduction on each subset. Both an offline objective reduction method and an online objective reduction method are developed. 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Existing objective reduction methods identify one set of essential objectives using an approximate nondominated front. However, if the Pareto front (PF) of a many-objective optimization problem (MaOP) is irregular, one single set of essential objectives may not be efficient for objective reduction. This article proposes to produce several different sets of essential objectives in objective reduction. More specifically, we use the Gaussian mixture model clustering to classify the obtained nondominated front into different subsets and perform objective reduction on each subset. Both an offline objective reduction method and an online objective reduction method are developed. The experimental results indicate that our proposed methods work well for MaOPs with degenerate or nondegenerate PFs.</description><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Degenerate Pareto front (PF)</subject><subject>Gaussian mixture model</subject><subject>many-objective optimization</subject><subject>Mixtures</subject><subject>Multiple objective analysis</subject><subject>Object recognition</subject><subject>objective reduction</subject><subject>Optimization</subject><subject>Probabilistic models</subject><subject>Reduction</subject><subject>Search problems</subject><subject>Sun</subject><subject>System analysis and design</subject><issn>1089-778X</issn><issn>1941-0026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtLw0AUhQdRsFZ_gLgZcJ06j2QeSym1Ci0BqeJuyGTuyJSY1JlE9N-b2uLqnsU553I-hK4pmVFK9N1m8TqfMcLYjFOhFBcnaEJ1TjNCmDgdNVE6k1K9naOLlLaE0LygeoJWpfdNaAFXrcNl-ydLu4W6D1-An8ENo-pa_BUqvKyGlELV4nX47ocIeN05aPC8GVIPMbTvl-jMV02Cq-OdopeHxWb-mK3K5dP8fpXVTPM-s8QXPteeQJGDLaTiteOOSme1Au7HOYTYHJTV44LCKeGsKiqvCqYsE7ngU3R76N3F7nOA1JttN8R2fGmY1JwRlUs6uujBVccupQje7GL4qOKPocTsoZk9NLOHZo7QxszNIRMA4N-vpSCSCP4LNYZn6A</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Li, Genghui</creator><creator>Wang, Zhenkun</creator><creator>Zhang, Qingfu</creator><creator>Sun, Jianyong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Existing objective reduction methods identify one set of essential objectives using an approximate nondominated front. However, if the Pareto front (PF) of a many-objective optimization problem (MaOP) is irregular, one single set of essential objectives may not be efficient for objective reduction. This article proposes to produce several different sets of essential objectives in objective reduction. More specifically, we use the Gaussian mixture model clustering to classify the obtained nondominated front into different subsets and perform objective reduction on each subset. Both an offline objective reduction method and an online objective reduction method are developed. 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| subjects | Clustering Clustering algorithms Degenerate Pareto front (PF) Gaussian mixture model many-objective optimization Mixtures Multiple objective analysis Object recognition objective reduction Optimization Probabilistic models Reduction Search problems Sun System analysis and design |
| title | Offline and Online Objective Reduction via Gaussian Mixture Model Clustering |
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