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• W2895004623 abstract "Cuckoo search (CS) is a one of the most efficient evolutionary for global optimization, and widely applied to solve diverse real-world problems. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploitation and exploration. To cope with these issues, a new CS extension based on individual knowledge learning (IKL-CS) is proposed. In this study, knowledge learning based on individual history is introduced into the CS algorithm. Individuals are constantly adjusted and optimized to use their historical knowledge in the optimization process, and communicate with each other to use their own knowledge. The accuracy and performance of the proposed approach are evaluated by eighteen classic benchmark functions. Statistical comparisons of our experimental results showed that the proposed IKL-CS algorithm made an appropriate trade-off between exploration and exploitation. Comparing the proposed I-PKL-CS with various evolutionary CS algorithms, the results demonstrated that IKL-CS is a competitive new type of algorithm." @default.
• W2895004623 created "2018-10-12" @default.
• W2895004623 creator A5027451971 @default.
• W2895004623 creator A5065766320 @default.
• W2895004623 creator A5091818964 @default.
• W2895004623 date "2018-01-01" @default.
• W2895004623 modified "2023-09-28" @default.
• W2895004623 title "Cuckoo Search Algorithm Based on Individual Knowledge Learning" @default.
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• W2895004623 doi "https://doi.org/10.1007/978-981-13-2829-9_41" @default.
• W2895004623 hasPublicationYear "2018" @default.
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