MATEC Web of Conferences
Volume 40, 20162015 International Conference on Mechanical Engineering and Electrical Systems (ICMES 2015)
|Number of page(s)||6|
|Section||Computer information technology and application|
|Published online||29 January 2016|
- Yang, Xin-She, and Suash Deb, Cuckoo search via Lévy flights, IEEE World Congress on Nature & Biologically Inspired Computing (NaBIC),pp:210–214(2009) [CrossRef] [Google Scholar]
- Yang, Xin-She and Suash Deb, Engineering optimisation by cuckoo search, International Journal of Mathematical Modelling and Numerical Optimisation 1(4):330–343(2010) [CrossRef] [Google Scholar]
- Yang X S and Deb S, Multiobjective cuckoo search for design optimization, Computers & Operations Research, 40(6):1616–1624(2013) [CrossRef] [Google Scholar]
- Gandomi A H, Yang X S and Alavi A H, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Engineering with Computers, 29(1):17–35(2013) [CrossRef] [Google Scholar]
- Wen Long, Ximing Liang, Yafei Huang and Yixiong Chen, An effective hybrid cuckoo search algorithm for constrained global optimization, Neural Comput & Applic 25(34):1577–1586(2014) [Google Scholar]
- Radova R. Bulatovic, Goran Boskovic, Mile M. Savkovic and Milomir M. G, Improved Cuckoo Search(ICS) algorthm for constrained optimization problems, Latin American Journal of Solids and Structures 11(8):1349–1362(2014) [Google Scholar]
- Parsopoulos K E and Vrahatis, M N, Particle swarm optimization method for constrained optimization problems, Intelligent Technologies–Theory and Application: New Trends in Intelligent Technologies, 76: 214–220(2002) [Google Scholar]
- Valian E, Tavakoli S, Mohanna S and Haghi A, Improved cuckoo search for reliability optimization problems, Computers & Industrial Engineering, 64(1):459–468 (2013) [CrossRef] [Google Scholar]
- Runarsson T P and Yao X, Stochastic ranking for constrained evolutionary optimization, IEEE Transactions on Evolutionary Computation, 4(3):284–294(2000) [CrossRef] [Google Scholar]
- Mezura Montes E and Coello Coello C A, A simple multimembered evolution strategy to solve constrained optimization problems, IEEE Transactions on Evolutionary Computation, 9(1):1–17 (2005) [CrossRef] [Google Scholar]
- Mezura-Montes E and Cetina-Domínguez O, Empirical analysis of a modified artificial bee colony for constrained numerical optimization, Applied Mathematics and Computation, 218(22):10943–10973(2012) [CrossRef] [Google Scholar]
- Wang L and Li L, An effective differential evolution with level comparison for constrained engineering design, Structural and Multidisciplinary Optimization 41(6):947–963(2010) [CrossRef] [Google Scholar]
- Zhang M, Luo W and Wang X, Differential evolution with dynamic stochastic selection for constrained optimization, Information Sciences 178(15):3043–3074(2008) [CrossRef] [Google Scholar]
- He Q and Wang L, A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Applied Mathematics and Computation, 186(2):1407–1422(2007) [CrossRef] [Google Scholar]
- Sadollah A, Bahreininejad A, Eskandar H and Mohd Hamdi, Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems, Applied Soft Computing 13(5):2592–2612(2013) [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.