Abstract
Swarm intelligence optimization algorithm is an important technology that solves the complex optimization problem by simulating the behavior of biological groups in nature. Monarch butterfly optimization (MBO) algorithm is such a swarm intelligence algorithm that simulates the migration behavior of the monarch butterflies in nature. It has received great success on solving single-objective optimization problems, but few contributions on multi-objective problems. In this paper, we modify MBO to solve multi-objective problems, and then propose a new multi-objective optimization algorithm based by combining effective strategies from other swarm-based algorithms. A series of benchmark functions are employed to evaluate the performance of this algorithm. We compare the experimental results with three basic algorithms and state-of-the-art algorithms. It is shown that the proposed algorithm performs better than the compared algorithms on most of the benchmark functions.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61672122, No. 61602077), the Public Welfare Funds for Scientific Research of Liaoning Province of China (No. 20170005), the Natural Science Foundation of Liaoning Province of China (No. 20170540097), and the Fundamental Research Funds for the Central Universities (No. 3132016348, No. 3132018194).
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Hu, R., Gao, J., Chen, R., Jiang, J. (2020). A Multi-objective Optimization Algorithm Based on Monarch Butterfly Optimization. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_17
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DOI: https://doi.org/10.1007/978-3-030-32456-8_17
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