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- W206277978 abstract "For the multiobjective problems, some global search methods may fail to find the Pareto optima with both accuracy and diversity. To pursue the two goals at the same time, a new memetic multiobjective differential evolution algorithm (MMODE) is proposed to hybridize the local search with differential evolution (DE) algorithm. The local search is conducted in an independent population to accelerate the search process, while DE can maintain the diversity. In MMODE, we use a new multiobjective Pareto differential evolution (MOPDE). Experimental results show that the MMODE performs better than other two MODEs in respects of the accuracy and diversity, especially for the multimodal functions." @default.
- W206277978 created "2016-06-24" @default.
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- W206277978 date "2013-01-01" @default.
- W206277978 modified "2023-09-27" @default.
- W206277978 title "MMODE: A Memetic Multiobjective Differential Evolution Algorithm" @default.
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- W206277978 doi "https://doi.org/10.1007/978-3-642-38703-6_50" @default.
- W206277978 hasPublicationYear "2013" @default.
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