Performance measured and particle swarm methods for dynamic multiobjective optimization problems

Li, X, Branke, J and Kirley, M 2007, 'Performance measured and particle swarm methods for dynamic multiobjective optimization problems', in Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO'07), London, England, UK, 7-11 July 2007.


Document type: Conference Paper
Collection: Conference Papers

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Title Performance measured and particle swarm methods for dynamic multiobjective optimization problems
Author(s) Li, X
Branke, J
Kirley, M
Year 2007
Conference name Genetic and Evolutionary Computation Conference 2007
Conference location London, England, UK
Conference dates 7-11 July 2007
Proceedings title Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO'07)
Publisher Association for Computing Machinery
Place of publication New York, USA
Abstract Introduction: Multiobjective optimization represents an important class of optimization techniques which have a direct implication for solving many real-world problems. In recent years, using evolutionary algorithms to solve multiobjective optimization problems, commonly known as EMO (Evolutionary Multi-objective Optimization), has gained rapid popularity. Since Evolutionary Algorithms (EAs) make use of a population of candidate solutions, a diverse set of optimal solutions so called Pareto-optimal solutions can be found within a single run. EAs offer a distinct advantage over many traditional optimization methods where multiple solutions must be found in multiple separate runs.
Subjects Optimisation
Keyword(s) algorithms
performance
experimentation
DOI - identifier 10.1145/1276958.1277137
Copyright notice Copyright is held by the author/owner(s).
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