Abstract
In recent years, the cuckoo search (CS) algorithm has been successfully applied to single-objective optimization problems. But in real life, most optimization problems are multi-objective optimization problems (MOPs). In order to enable CS to better solve MOPs, this paper proposes an elite-guided multi-objective cuckoo search algorithm based on cross-operation and information enhancement (CIE-MOCS). This algorithm first enhances its population diversity through crossover operation, then adds elite individuals to guide its update process to speed up the algorithm convergence speed. Finally, the method of information enhancement is adopted in the abandonment process, so that the algorithm is not easy to fall into the local optimum. In order to verify the performance of the algorithm, this paper uses a variety of benchmark functions and performance evaluation indicators to evaluate it, and provides a case to verify the effectiveness of the algorithm in practical applications. The experimental results show that CIE-MOCS has good performance compared with the contrasting algorithms.
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Funding
This work is supported by the 1. National Natural Science Foundation of China (Grant No. 62073281), 2. the Hebei Provincial Natural Science Foundation (Grant No. F2022203088), 3. the Hebei Provincial Science and Technology Plan Project (Grant No. 19211602D), 4. the Second Batch of Youth Top-notch Talent Support Program in Hebei Province (Grant No. 5040050).
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Yang, X., Hao, X., Yang, T. et al. Elite-guided multi-objective cuckoo search algorithm based on crossover operation and information enhancement. Soft Comput 27, 4761–4778 (2023). https://doi.org/10.1007/s00500-022-07605-8
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DOI: https://doi.org/10.1007/s00500-022-07605-8