布谷鸟搜索算法

布谷鸟搜索(Cuckoo Search,缩写 CS),也叫杜鹃搜索,是由剑桥大学杨新社(音译自:Xin-She Yang)教授和S.戴布(S.Deb)于2009年提出的一种新兴启发算法[1]

CS算法是通过模拟某些种属布谷鸟的寄生育雏(Brood Parasitism) [2],来有效地求解最优化问题的算法[3]。同时,CS也采用相关的Levy飞行搜索机制。研究表明,布谷鸟搜索比其他群体优化算法更有效[4]

布谷鸟搜索

布谷鸟搜索(CS)使用蛋巢代表解。最简单情况是,每巢有一个蛋,布谷鸟的蛋代表了一种新的解。其目的是使用新的和潜在的更好的解,以取代不那么好的解。该算法基于三个理想化的规则:

  • 每个杜鹃下一个蛋,堆放在一个随机选择的巢中;
  • 最好的高品质蛋巢将转到下一代;
  • 巢的数量是固定的,布谷鸟的蛋被发现的概率为 

实际应用

布谷鸟搜索到工程优化问题中的应用已经表现出其高优效率[5] 经过几年的发展,为了进一步提高算法的性能,CS算法的很多变体与改进逐步涌现。瓦尔顿(Walton)等提出了修正布谷鸟搜索(Modified Cuckoo Search,缩写 MCS);伐立安(Valian)等提出了一种可变参数的改进CS算法,提高了收敛速度,并将改进算法应用于前馈神经网络训练中[6];马里切尔凡姆(Marichelvam)将一种混合CS算法应用于流水车间调度问题求解中[7];钱德拉塞卡兰(Chandrasekaran)等将集成了模糊系统的混合CS算法应用于机组组合问题[8]

杨(Yang)和戴布(Deb)提出多目标布谷鸟搜索(Multiobjective Cuckoo Search,缩写 MOCS),应用到工程优化并取得很好的效果[9];詹(Zhan)等通过对种群分组,并根据搜索的不同阶段对搜索步长进行预先设置,提出了修正调适布谷鸟搜索(Modified Adaptive Cuckoo Search,缩写 MACS),提高了CS的性能[10]

参考文献

  1. ^ X. S. Yang and S. Deb, Cuckoo search via Lévy flights, in: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), IEEE Publications,pp. 210-214.
  2. ^ R. B. Payne, M. D. Sorenson, and K. Klitz, The Cuckoos, Oxford University Press, (2005).
  3. ^ X.S. Yang and S. Deb, Engineering optimisation by cuckoo search, Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, 330-343 (2010).
  4. ^ Novel Cuckoo Search Beats Particle Swarm Optimization, http://www.scientificcomputing.com/news-DA-Novel-Cuckoo-Search-Algorithm-Beats-Particle-Swarm-Optimization-060110.aspx页面存档备份,存于互联网档案馆
  5. ^ Gandomi A, Yang X, Alavi A. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems[J]. Engineering with Computers, Vol. 29, 17-35 (2013).
  6. ^ E. Valian, S. Mohanna and S. Tavakoli, Improved cuckoo search algorithm for feedforward neural network training, Int. J. Artificial Intelligence and Applications, Vol. 2, No. 3, 36-43(2011).
  7. ^ M.K.Marichelvam, An improved hybrid Cuckoo Search (IHCS) metaheuristics algorithm for permutation flow shop scheduling problems, International Journal of Bio-Inspired Computation, Vol. 4, No. 4, 200-205 (2012).
  8. ^ K. Chandrasekaran and S. P. Simon, Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm, Swarm and Evolutionary Computation, Vol. 5, 1-16 (2012).
  9. ^ X. S. Yang and S. Deb, Multiobjective cuckoo search for design optimization, Computers and Operations Research, Vol. 40, No. 6, 1616-1624 (2013).
  10. ^ Y. Zhang, L. Wang, Q. Wu, Modified Adaptive Cuckoo Search Algorithm and Formal Description for Global Optimization, International Journal of Computer Applications in Technology, Vol. 44, No. 2, 73-79 (2012).

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