Abstract
In order to solve complex optimization problems, a novel intelligence algorithm called quantum-inspired teaching-learning-based optimization method (QTLBO) is proposed. By hybridizing teaching-learning-based optimization (TLBO) and quantum computing theory, QTLBO can be well evolved by simulated quantum rotation gate operation. Then QTLBO is used to resolve linear array pattern synthesis problems. Simulation results are provided to show that the proposed QTLBO method can solve this kind of problems efficiently and give out a superior solution than those obtained by previous classical intelligence optimization methods. The proposed QTLBO method can search for the global optimal solution of linear array pattern synthesis problems.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (61571149) and the Fundamental Research Funds for the Central Universities (HEUCFP201772).
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Gao, H.Y., Zhang, X.T., Du, Y.N., Diao, M. (2019). Quantum-Inspired Teaching-Learning-Based Optimization for Linear Array Pattern Synthesis. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_257
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DOI: https://doi.org/10.1007/978-981-10-6571-2_257
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