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Adaptive Distributed Differential Evolution
2019
IEEE Transactions on Cybernetics
Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation
doi:10.1109/tcyb.2019.2944873
pmid:31634855
fatcat:uptzut7kdvd6jdjy2nklcmacfe