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- W2172135250 abstract "In the recent past, hybrid metaheuristics became famous as successful optimization methods. The motivation for the hybridization is a notion of combining the best of two worlds: evolutionary black box optimization and local search. Successful hybridizations in large combinatorial solution spaces motivate to transfer the idea of combining the two worlds to continuous domains as well. The question arises: Can local search also improve the convergence to the Pareto front in continuous multiobjective solutions spaces? We introduce a relay and a concurrent hybridization of the successful multiobjective optimizer SMS-EMOA and local optimization methods like Hooke & Jeeves and the Newton method. The concurrent approach is based on a parameterized probability function to control the local search. Experimental analyses on academic test functions show increased convergence speed as well as improved accuracy of the solution set of the new hybridizations." @default.
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- W2172135250 date "2009-07-08" @default.
- W2172135250 modified "2023-09-26" @default.
- W2172135250 title "On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization" @default.
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- W2172135250 doi "https://doi.org/10.1145/1569901.1569985" @default.
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