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Intelligent Search Algorithm Design

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Abstract

With the development of the optimization theory, some new intelligent algorithms have been rapidly developed and widely used, and these algorithms have become new methods to solve the traditional system identification problems, such as genetic algorithm, ant colony algorithm, particle swarm optimization algorithm, differential evolution algorithm. These optimization algorithms simulate natural phenomena and processes.

Keywords

Final Optimization Length Energy Function Changes City Hold License V3 Xlabel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    J. Kennedy, R. Eberhart, Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)Google Scholar
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    R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
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    J.J. Hopfield, D.W. Tank, Neural computation of decision in optimization problems. Biol. Cybernrtics 52, 141–152 (1985)zbMATHGoogle Scholar
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    S.Y. Sun, J.L. Zheng, A modified algorithm and theoretical analysis for hopfield neural solving TSP. Acta Electronica Sinca 23(1), 73–78 (1995). (in Chinese)Google Scholar

Copyright information

© Tsinghua University Press, Beijing and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Beihang UniversityBeijingChina

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