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Evolutionary Multiobjective Clustering
2004new approach to data clustering is proposed, in which two or more measures of cluster quality are simultaneously optimized using a multiobjective evolutionary algorithm (EA). For this purpose, the PESA-II EA is adapted for the clustering problem by the incorporation of specialized mutation and initialization procedures, described herein.
Handl, Julia, Knowles, Joshua
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Multiobjective Lévy-Flight Firefly Algorithm for Multiobjective Optimization
2021The firefly algorithm (FA) was firstly proposed during 2008–2009 as one of the powerful population-based metaheuristic optimization techniques for solving continuous and combinatorial optimization problems. The FA has been proved and applied to various real-world problems in mostly single objective optimization manner. However, many real-world problems
Somchai Sumpunsri +2 more
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2014
Optimization problems with multiple criteria measuring solution quality can be modeled as multiobjective programming problems. Because the objective functions are usually in conflict, there is not a single feasible solution that can optimize all objective functions simultaneously. An optimal solution is one that is most preferred by the decision maker (
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Optimization problems with multiple criteria measuring solution quality can be modeled as multiobjective programming problems. Because the objective functions are usually in conflict, there is not a single feasible solution that can optimize all objective functions simultaneously. An optimal solution is one that is most preferred by the decision maker (
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213 Multiobjective Clustering by Multiobjective Genetic Algorithm
The Proceedings of OPTIS, 2006Nobukazu MATAKE +2 more
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Multiobjective Complementary Control
IEEE Transactions on Automatic ControlJiapeng Xu +3 more
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