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dc.contributor.authorRakshit, P.
dc.contributor.authorChowdhury, A.
dc.contributor.authorKonar, A.
dc.contributor.authorNagar, A.K.
dc.description.abstractThe paper proposes a novel extension of many objective optimization using differential evolution (MaODE). MaODE solves a many objective optimization (MaOO) problem by parallel optimization of individual objectives. MaODE involves N populations, each created for an objective to be optimized using MaODE. The only mode of knowledge transfer among populations in MaODE is the modified version of mutation policy of DE, where every member of the population during mutation is influenced by the best members of all the populations under consideration. The present work aims at further increasing the communication between the members of the population by communicating between a superior and an inferior population, using a novel migration strategy. The proposed migration policy enables poor members of an inferior population to evolve with a superior population. Simultaneously, members from the superior population are also transferred to the inferior one to help it improving its performance. Experiments undertaken reveal that the proposed extended version of MaODE significantly outperforms its counterpart and the state-of-the-art techniques.en_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.subjectdifferential evolutionen_US
dc.subjectmany-objective optimizationen_US
dc.subjectindividual parallel optimizationen_US
dc.subjectmultiple populationen_US
dc.titleMigration in Multi-Population Differential Evolution for Many Objective Optimizationen_US
dc.journal.title2020 IEEE Congress on Evolutionary Computation (CEC)en_US

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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España