Abstract
Backtracking search algorithm is a promising stochastic search technique by using its historical information to guide the population evolution. Using historical population information improves the exploration capability, but slows the convergence, especially on the later stage of iteration. In this paper, a best guided backtracking search algorithm, termed as BGBSA, is proposed to enhance the convergence performance. BGBSA employs the historical information on the beginning stage of iteration, while using the best individual obtained so far on the later stage of iteration. Experiments are carried on the 28 benchmark functions to test BGBSA, and the results show the improvement in efficiency and effectiveness of BGBSA.
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Acknowledgments
This work was supported by the NSFC Joint Fund with Guangdong of China under Key Project U1201258, the National Natural Science Foundation of China under Grant No. 61573219, the Shandong Natural Science Funds for Distinguished Young Scholar under Grant No. JQ201316, the Natural Science Foundation of Fujian Province of China under Grant No. 2016J01280 and the Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.
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Zhao, W., Wang, L., Wang, B., Yin, Y. (2016). Best Guided Backtracking Search Algorithm for Numerical Optimization Problems. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_33
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DOI: https://doi.org/10.1007/978-3-319-47650-6_33
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