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| {{about|the search algorithm|the hashing algorithm|cuckoo hashing}}
| | Japanese chef knives are created with the most superb steel materials that are discovered there. With hammer forged knives you can see the blacksmith's art in the person patterns he creates by manipulating billets of damascus steel. Carbon steel is prone to rust and tarnish so if you are not prepared to look immediately after your knives probably stainless steel knives are better for you. Blades made from carbon steel can be sharpened effortlessly to a razor edge. But, knives created of carbon steel are prone to rusting. These days, improved kitchen knives are created of higher-carbon steel. I got my Shun knives there.<br><br><br><br>There are lots of finest excellent kitchen knives out there that are completely forged. If you want sturdy kitchen knives you should stick to the forged lines which offer you premium high-quality. If you want the more affordable kitchen knives, you can take a look at the origins of the cutlery to trace their companies. Normally some kitchen knives are made in other nations apart from exactly where they are being sold. Rada Cutlery has a wide array of kitchen knives they sell.<br><br>The reason is for the reason that not sharpened knives have a tendency to slip by one side when you slice, so you [http://Photo.net/gallery/tag-search/search?query_string=danger+cutting danger cutting] your fingers. Don't forget , in order to retain the sharpness of the knife for a long time use a steel sharpener anytime you get started or finish cutting. Lastly, I have to say that you wont turn into a actual expert only by having the most current knives on the by reading cooking books or watching tons of guiding videos.<br><br>The Santoku knife is a basic all purpose kitchen knife which may be employed in chopping up bigger vegetables such as potatoes, cabbage and pumpkins. If you are prepared to invest in a knife that will last you a life time then head over to the Shun knives evaluation weblog There you will discover testimonials and fantastic prices on Shun Japanese knives. You will normally uncover that you will use these knives the most.<br><br>If the size of the blade is huge, it would in all probability be heavier making it tough to carry it around and also hard manage when in use. When you loved this article and you would love to receive much more information concerning [http://www.thebestkitchenknivesreviews.com/best-knife-set-reviews-top-kitchen-sets/ Best Chef Knife Set Brands] please visit our own web-page. Many folks do not select folding knives of the fear that the hinge may well loosen up or there may well be other difficulty with it. This sort is pretty compact and easier to carry. They are significantly less sturdy compared to fixed blade knives. Butterfly knives have two handles that have cavities that residence the knife cutting edges.<br><br>Turning knife - You won't normally obtain these in dwelling kitchen but an necessary if you are [http://Www.Dailymail.Co.uk/home/search.html?sel=site&searchPhrase=producing producing] a exclusive style presentation. Always keep the knives separate and individually contained, wrapped or held with the point of the knives secured, this consists of in your toolbox or even in the picnic basket. A good job i just found this subject and it is exciting I am begining to be passionate about kitchen. An awesome kitchen tool, the high-quality and functionality far outweighs the value tag. Start off in your kitchen.<br><br>Kitchen knives will require sharpening right after three to five months of regular use. Use a sharpening stone, sharpening steel, V-shaped knife sharpener or an electric sharpener. Most chef's knives have blades that are 6, 8 or ten inches extended. Although a 10-inch knife can absolutely chop, cut and prepare most any food item, its size can be daunting to some cooks. Kitchen knifes with 8-inch blades typically are preferred, as they can get most of the kitchen tasks done and their size is quite manageable. |
| '''Cuckoo search (CS)''' is an [[Optimization (mathematics)|optimization]] [[algorithm]] developed by [[Xin-she Yang]] and Suash Deb | |
| in 2009.<ref name=NaBIC>{{cite conference | |
| | title =Cuckoo search via Lévy flights
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| | author =X.-S. Yang
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| | coauthors =S. Deb
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| | month =December
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| | year =2009
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| | conference =World Congress on Nature & Biologically Inspired Computing (NaBIC 2009)
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| | publisher =IEEE Publications
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| | pages =210–214
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| | arxiv =1003.1594v1 | accessdate =March 10, 2010
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| }} [http://papercore.org/Yang2009 Papercore summary http://papercore.org/Yang2009]
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| </ref><ref>{{cite web|author=Inderscience |url=http://www.alphagalileo.org/ViewItem.aspx?ItemId=76985&CultureCode=en |title=Cuckoo designs spring |publisher=Alphagalileo.org |date=27 May 2010 |accessdate=2010-05-27}}</ref> It was inspired by the [[Obligate parasite|obligate brood parasitism]] of some [[cuckoo]] species by laying their eggs in the nests of other host birds (of other species). Some host birds can engage direct conflict with the intruding cuckoos. For example, if a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Some cuckoo species such as the [[New World]] brood-parasitic [[Tapera]] have evolved in such a way that female parasitic cuckoos are often very specialized in the mimicry in colors and pattern of the eggs of a few chosen host species <ref>R. B. Payne, M. D. Sorenson, and K. Klitz, The Cuckoos, Oxford University Press, (2005).</ref>
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| Cuckoo search idealized such breeding behavior, and thus can be applied for various optimization problems. It seems that it can outperform other metaheuristic algorithms in applications.<ref>http://www.scientificcomputing.com/news-DA-Novel-Cuckoo-Search-Algorithm-Beats-Particle-Swarm-Optimization-060110.aspx</ref>
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| Cuckoo search (CS) uses the following representations:
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| Each egg in a nest represents a solution, and a cuckoo egg represents a new solution. The aim is to use the new and potentially better solutions (cuckoos) to replace a not-so-good solution in the nests. In the simplest form, each nest has one egg. The algorithm can be extended to more complicated cases in which each nest has multiple eggs representing a set of solutions.
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| CS is based on three idealized rules:
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| # Each cuckoo lays one egg at a time, and dumps its egg in a randomly chosen nest;
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| # The best nests with high quality of eggs will carry over to the next generation;
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| # The number of available hosts nests is fixed, and the egg laid by a cuckoo is discovered by the host bird with a probability <math>p_a \in (0,1)</math>. Discovering operate on some set of worst nests, and discovered solutions dumped from farther calculations.
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| In addition, Yang and Deb discovered that the random-walk style search is better performed by [[Lévy flight]]s rather than simple [[random walk]].
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| The [[pseudo-code]] can be summarized as:
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| Objective function: <math>f(\mathbf{x}), \quad \mathbf{x}=(x_1,x_2,\dots,x_d); \, </math> | |
| Generate an initial population of <math> n </math> host nests;
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| While (t<MaxGeneration) or (stop criterion)
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| Get a cuckoo randomly (say, i) and replace its solution by performing Lévy flights;
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| Evaluate its quality/fitness <math>F_i </math>
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| [For maximization, <math>F_i \propto f(\mathbf{x}_i)</math> ];
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| Choose a nest among n (say, j) randomly;
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| if (<math>F_i>F_j </math>),
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| Replace j by the new solution;
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| end if
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| A fraction (<math>p_a</math>) of the worse nests are abandoned and new ones are built;
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| Keep the best solutions/nests;
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| Rank the solutions/nests and find the current best;
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| Pass the current best solutions to the next generation;
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| end while
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| An important advantage of this algorithm is its simplicity. In fact, comparing with other population- or agent-based [[metaheuristic]] algorithms such as [[particle swarm optimization]] and [[harmony search]], there is essentially only a single parameter <math>p_a</math> in CS (apart from the population size <math>n</math>). Therefore, it is very easy to implement.
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| ==Random walks and the step size ==
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| An important issue is the applications of Lévy flights and random walks in the generic equation for generating new solutions
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| : <math> \mathbf{x}_{t+1}=\mathbf{x}_t + s E_t, </math>
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| where <math> E_t </math> is drawn from a standard normal distribution with zero mean and unity standard deviation for random walks, or drawn from Lévy distribution for Lévy flights. Obviously, the random walks can also be linked with the similarity between a cuckoo's egg and the host's egg which can be tricky in implementation. Here the step size <math>s</math> determines how far a random walker can go for a fixed number of iterations. The generation of Lévy step size is often tricky, and a good algorithm is Mantegna's algorithm.<ref>R. N. Mantegna, Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes, Physical Review E, Vol.49, 4677-4683 (1994).</ref>
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| If s is too large, then the new solution generated will be too far away from the old solution (or even jump out side of the bounds). Then, such a move is unlikely to be accepted. If s is too small, the change is too small to be significant, and consequently such search is not efficient. So a proper step size is important to maintain the search as efficient as possible. | |
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| As an example, for simple isotropic random walks, we know that the average distance <math> r </math> traveled in the d-dimension space is
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| : <math> r^2=2 d D t, </math>
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| where <math>D=s^2/2\tau</math> is the effective diffusion coefficient. Here <math> s</math> is the step size or distance traveled at each jump, and <math> \tau </math> is the time taken for each jump. The above equation implies that<ref>X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).</ref>
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| : <math> s^2=\frac{\tau \; r^2}{t \; d}. </math>
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| For a typical length scale L of a dimension of interest, the local search is typically limited in a region of <math>r=L/10 </math>. For <math>\tau=1 </math> and t=100 to 1000, we have <math>s\approx 0.01L </math> for d=1, and <math>s \approx 0.001L </math> for d=10. Therefore, we can use s/L=0.001 to 0.01 for most problems. Though the exact derivation may require detailed analysis of the behaviour of Lévy flights.<ref>M. Gutowski, Lévy flights as an underlying mechanism for global optimization algorithms, ArXiv Mathematical Physics e-Prints, June, (2001).</ref>
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| Algorithm and convergence analysis will be fruitful, because there are many open problems related to metaheuristics<ref>X. S. Yang, Metaheuristic optimization: algorithm analysis and open problems, in: Experimental Algorithms (SEA2011), Eds (P. M. Pardalos and S. Rebennack), LNCS 6630, pp.21-32 (2011).</ref>
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| == Implementation ==
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| The pseudo code was given in a sequential form, but Yang and Deb provides a vectorized implementation which is very efficient.<ref>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). http://arxiv.org/abs/1005.2908</ref> In the real world, if a cuckoo's egg is very similar to a host's eggs, then this cuckoo's egg is less likely to be discovered, thus the fitness should be related to the difference in solutions. Therefore, it is a good idea to do a random walk in a biased way with some random step sizes. For Matlab implementation, this biased random walk can partly be achieved by
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| :stepsize=rand*(nest(randperm(n),:)-nest(randperm(n),:));
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| :new_nest=nest+stepsize.*K;
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| where K=rand(size(nest))>pa and pa is the discovery rate. A standard CS matlab can be found here http://www.mathworks.com/matlabcentral/fileexchange/29809-cuckoo-search-cs-algorithm.<ref>[http://www.mathworks.com/matlabcentral/fileexchange/29809-cuckoo-search-cs-algorithm Matlab demo code]</ref>
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| There is some controversy on whether or not this implementation truly reflects the originally published cuckoo search algorithm as the random walk closely resembles another optimization algorithm, differential evolution. It was shown that the 'biased random walk' has a larger impact on the performance of the algorithm than anything else.<ref>{{cite journal|last=S. Walton|coauthors=O. Hassan, M.R. Brown and K. Morgan |title=Comment on Paper by Bhargava, Fateen and Bonilla-Petriciolet|journal=Fluid Phase Equilibria|date=24 May 2013|doi=10.1016/j.fluid.2013.05.011}}</ref>
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| An object-oriented software implementation of cuckoo search was provided by Bacanin<ref>N. Bacanin, An object-oriented software implementation of a novel cuckoo search algorithm, Proc. of the 5th European Conference on European Computing Conference (ECC'11), pp. 245-250 (2011).</ref> On the other hand, a modified cuckoo search algorithm is also implemented for unconstrained optimization problems.<ref>M. Tuba, M. Subotic, and N. Stanarevic, Modified cuckoo search algorithm for unconstrained optimization problems, Proc. of the 5th European Conference on European Computing Conference (ECC'11), pp. 263-268 (2011).</ref>
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| An open source implementation of Modified Cuckoo Search can be found here http://code.google.com/p/modified-cs/
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| ==Modified cuckoo search==
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| A modification of the standard Cuckoo Search was made by Walton et al.<ref>{{cite journal|last=S. Walton|coauthors=O. Hassan, K. Morgan and M.R. Brown|title=Modified cuckoo search: A new gradient free optimisation algorithm|journal=Chaos, Solitons & Fractals|date=30 June 2011|doi=10.1016/j.chaos.2011.06.004}}</ref> with the aim to speed up convergence. The modification involves the additional step of information exchange between the top eggs. It was shown that Modified Cuckoo Search (MCS) outperforms the standard cuckoo search and other algorithms, in terms of convergence rates, when applied to a series of standard optimization benchmark objective functions.
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| Subsequently, the modified cuckoo search has been applied to optimize unstructured mesh which reduces computational cost significantly.<ref>S. Walton, O. Hassan, K. Morgan, Using proper orthogonal decomposition to reduce the order of optimization problems, in: Proc. 16th Int. Conf. on Finite Elrments in Flow Problems (Eds. Wall W.A. and Gvravemeier V.), Munich, p.90 (2011).</ref><ref>Walton, S., Hassan, O. and Morgan, K. (2012), Reduced order mesh optimisation using proper orthogonal decomposition and a modified cuckoo search. Int. J. Numer. Meth. Engng.. doi: 10.1002/nme.4400</ref> In addition, another interesting improvement to cuckoo search is the so-called quantum-inspired cuckoo search with convincing results <ref>A. Layeb, A novel quantum inspired cuckoo search for knapsack problems, Int. J. Bio-Inspired Computation, Vol. 3, 297-305 (2011).</ref>
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| ==Multiobjective cuckoo search (MOCS)==
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| A multiobjective cuckoo search (MOCS) method has been formulated to deal with multi-criteria optimization problems.<ref>X. S. Yang and S. Deb, Multiobjective cuckoo search for design optimization, Computers and Operations Research, October (2011). {{doi|10.1016/j.cor.2011.09.026}}</ref>
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| This approach uses random weights to combine multiple objectives to a single objective. As the weights vary randomly, Pareto fronts can be found so the points can distributed diversely over the fronts.
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| ==Hybridization==
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| Though cuckoo search is a swarm-intelligence-based algorithm, it can be still hybridized with other
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| swarm-based algorithms such as PSO. For example, a hybrid CS-PSO algorithm seems to remedy the defect of PSO.<ref>F. Wang, L. Lou, X. He, Y. Wang, Hybrid optimization algorithm of PSO and Cuckoo Search, in: Proc. of 2nd Int. Conference on Artificial Intelligence, Management Science and
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| Electronic Commerce (AIMSEC'11), pp. 1172-1175 (2011).</ref>
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| == Applications ==
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| The applications of Cuckoo Search into engineering optimization problems<ref>Sean Walton, Oubay Hassan, Kenneth Morgan, Selected Engineering Applications of Gradient Free Optimisation Using Cuckoo Search and Proper Orthogonal Decomposition, Archives of Computational Methods in EngineeringJune 2013, Volume 20, Issue 2, pp 123-154, http://link.springer.com/article/10.1007/s11831-013-9083-7</ref> have shown its promising efficiency. For example, for both spring design and welded beam design problems, CS obtained better solutions than existing solutions in literature. A promising discrete cuckoo search algorithm is recently proposed to solve nurse scheduling problem.<ref>Tein L. H. and Ramli R., Recent advancements of nurse scheduling models and a potential path, in: Proc. 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA 2010), pp. 395-409 (2010). http://research.utar.edu.my/CMS/ICMSA2010/ICMSA2010_Proceedings/files/statistics/ST-Lim.pdf</ref> An efficient computation approach based on cuckoo search has been proposed for data fusion in wireless sensor networks.<ref>M. Dhivya, M. Sundarambal, L. N. Anand, Energy Efficient Computation of Data Fusion in Wireless Sensor Networks Using Cuckoo Based Particle Approach (CBPA), Int. J. of Communications, Network and System Sciences, Vol. 4, No. 4, 249-255 (2011).</ref><ref>M. Dhivya and M. Sundarambal, Cuckoo search for data gathering in wireless sensor networks,Int. J. Mobile Communications, Vol. 9, pp. 642-656 (2011).</ref> Cuckoo search is adapted to solve NP-hard combinatorial optimization problems like [[travelling salesman problem]].<ref>A. Ouaarab, B. Ahiod, and X.-S. Yang, Discrete cuckoo search algorithm for the travelling salesman problem, Neural Computing and Applications, (2013). doi:10.1007/s00521-013-1402-2.</ref>
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| A new quantum-inspired cuckoo search was developed to solve Knapsack problems, which shows its effectiveness.<ref>A. Layeb, A novel quantum-inspired cuckoo search for Knapsack problems, Int. J. Bio-inspired Computation, Vol. 4, (2011).</ref>
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| Cuckoo search can also be used to efficiently generate independent test paths for structural software testing <ref>P. R. Srivastava, M. Chis, S. Deb and X. S. Yang, An efficient optimization algorithm for structural software testing, Int. J. Artificial Intelligence, Vol. 9 (S12), 68-77(2012)</ref> and test data generation.<ref>K. Perumal, J. M. Ungati, G. Kumar, N. Jain, R. Gaurav and P. R. Srivastava, Test data generation: a hybrid approach using cuckoo and tabu search,
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| Swarm, Evolutionary, and Memetic Computing (SEMCCO2011), Lecture Notes in Computer Sciences, Vol. 7077, 46-54 (2011)</ref>
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| A conceptual comparison of the cuckoo search with [[Particle swarm optimization]], [[Differential evolution]] and [[Artificial bee colony algorithm]] suggest that Cuckoo search and Differential evolution algorithms provide more robust results than Particle swarm optimization and Artificial bee colony algorithm.<ref>P. Civicioglu and E. Besdok, A conception comparison of the cuckoo search, particle swarm optimization, differential evolution and artificial bee colony algorithms, Artificial Intelligence Review, {{doi|10.1007/s10462-011-92760}}, 6 July (2011).</ref> An extensive detailed study of various structural optimization problems suggests that cuckoo search obtains better results than other algorithms.<ref>A. H. Gandomi, X. S. Yang, A. H. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,Engineering with Computers, Vol. 27, July (2011).</ref> In addition, a new software testing approach has been developed based on cuckoo search.<ref>K. Choudhary and G. N. Purohit, A new testing approach using cuckoo search to achieve multi-objective genetic algorithm, J. of Computing, Vol. 3, No. 4, 117-119 (2011).</ref> In addition, cuckoo search is particularly suitable for large scale problems, as shown in a recent study.<ref>E. R. Speed, Evolving a Mario agent using cuckoo search and softmax heuristics, Games Innovations Conference (ICE-GIC), pp. 1-7 (2010). {{doi|10.1109/ICEGIC.2010.5716893}}</ref> Cuckoo search has been applied to train neural networks with improved performance.<ref>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).</ref> Furthermore, CS is successfully applied to train spiking neural models.<ref>R. A. Vazquez, Training spiking neural models using cuckoo search algorithm, 2011 IEEE Congress on Evolutionary Computation (CEC'11), pp.679-686 (2011).</ref> Cuckoo search has also been used to optimize web service composition process and planning graphs.
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| <ref>V. R. Chifu, C. B. Pop, I. Salomie, D> S. Suia and A. N. Niculici, Optimizing the semantic web service composition process using cuckoo search, in: Intelligent Distributed Computing V, Studies in Computational Intelligence, Vol. 382, pp. 93-102 (2012).</ref>
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| Cuckoo search is a reliable approach for embedded system design<ref>A. Kumar and S. Chakarverty, Design optimization for reliable embedded system using Cuckoo Search,in: Proc. of 3rd Int. Conference on Electronics Computer Technology (ICECT2011), pp. 564-268 (2011).</ref> and design optimization<ref>A. Kumar and S. Chakarverty, Design optimization using genetic algorithm and Cuckoo Search, in: Proc. of IEEE Int. Conference on Electro/Information Technology (EIT), pp. 1-5 (2011).</ref> including optimum design of steel frames.<ref>A. Kaveh, T. Bakhshpoori, Optimum design of steel frames using cuckoo search algorithm with Lévy flights,Structural Design of Tall and Special Buildings, Vol. 21, online first 28 Nov 2011.http://onlinelibrary.wiley.com/doi/10.1002/tal.754/abstract</ref>
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| More recent studies suggest that cuckoo search can outperform other algorithms in milling applications,<ref>A. R. Yildiz, Cuckoo search algorithm for the selection of optimal machine parameters in milling operations, Int. J. Adv. Manuf. Technol., (2012). {{doi|10.1007/s00170-012-4013-7}}</ref> manufacturing scheduling,<ref>S. Burnwal, S. Deb, Scheduling optimization of flexible manufacturing system using cuckoo search-based approach, Int. J. Adv Manuf Technol, (2012).</ref> and others.<ref>H. Q. Zheng and Y. Zhou, A novel cuckoo search optimization algorithm based on Gauss distribution, J. Computational Information Systems, Vol. 8, 4193-4200 (2012).</ref> An interesting application of cuckoo search is to solve boundary value problems.<ref>A. Noghrehabadi, M. Ghalambaz and A. Vosough, A hybrid power series -- Cuckoo search optimization algorithm to electrostatic deflection of micro fixed-fixed actuators, Int. J. Multidisciplinary Science and Engineering, Vol. 2, No. 4,22-26 (2011).</ref>
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| == References ==
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| {{Reflist}}
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| {{collective animal behaviour}}
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| {{Optimization algorithms}}
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| [[Category:Optimization algorithms and methods]]
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| [[Category:Evolutionary algorithms]]
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