Abstract
It is well known that Differential Evolution (DE) algorithm has been widely applied to solve global optimization problems during the last decades. DE is usually criticized for the slow convergence. To improve the algorithm performance, we propose an algorithm called MSDE that utilizes a local search operator based on mean shift. In MSDE, one offspring solution is generated by the mean shift based search operator, and the others are created by the DE search operator. A test suite of 12 benchmark functions with different characteristics are chosen to evaluate our approach. The experimental results suggest that MSDE can successfully improve the performance of DE and have a faster convergence rate on the given test suite.
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Acknowledgment
This work is supported by China National Instrumentation Program under Grant No. 2012YQ180132, the National Natural Science Foundation of China under Grant No. 61273313 and No. 61673180, and the Science and Technology Commission of Shanghai Municipality under Grant No. 14DZ2260800.
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Fang, H., Zhou, A., Zhang, G. (2016). A Mean Shift Assisted Differential Evolution Algorithm. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_21
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DOI: https://doi.org/10.1007/978-981-10-3614-9_21
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