Abstract
Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) convert a multi-objective optimization problem (MOP) into a set of scalar sub-problems, which are then optimized collaboratively. This paper designs a multi-objective pigeon-inspired optimization algorithm based on decomposition (MPIO/D) to improve the search efficiency by using the mechanism behind the remarkable navigation capacity of homing pigeons. The map and compass operator can record the direction of descent (rising) to generate good offspring. The landmark operator is used to accelerate the convergence of sub-problems with poor convergence. Compared to six competitive MOEA/Ds, MPIO/D has shown the advantages in tacking two benchmark sets of MOPs.
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Acknowledgments
This work was supported by National Natural Science Foundations of China (no. 61806120, no. 61502290, no. 61401263, no. 61672334, no. 61673251), China Postdoctoral Science Foundation (no. 2015M582606), Industrial Research Project of Science and Technology in Shaanxi Province (no. 2015GY016, no. 2017JQ6063), Fundamental Research Fund for the Central Universities (no. GK202003071), Natural Science Basic Research Plan in Shaanxi Province of China (no. 2016JQ6045).
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Dai, C. (2021). A Multi-objective Pigeon-Inspired Optimization Algorithm Based on Decomposition. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_86
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DOI: https://doi.org/10.1007/978-3-030-69717-4_86
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