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- W2316351444 abstract "This paper proposes an Artificial Immune Network with the Cloud-based Mutation Operator (AINetCMO) for complex numerical optimization problems. Introducing the cloud model into the mutation operator is expected to enhance the convergence of AINet-CMO. In the mutation process, the mutation step length can be dynamically adapted to the evolution of candidate antibodies by measure of the cloud model. A series of numerical simulations are arranged to compare such performance indices as solution accuracy and convergence speed between AINet-CMO and other existing algorithms. The results indicate that AINet-CMO outperforms the other three artificial immune systems, i.e., opt-aiNet, IA-AIS and AAIS-2S." @default.
- W2316351444 created "2016-06-24" @default.
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- W2316351444 date "2014-07-01" @default.
- W2316351444 modified "2023-09-25" @default.
- W2316351444 title "Numerical Optimization Oriented Artificial Immune Network with Cloud-based Mutation Operator" @default.
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- W2316351444 doi "https://doi.org/10.12733/jics20104057" @default.
- W2316351444 hasPublicationYear "2014" @default.
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