Assignment on Cuckoo Search Algorithm

               Optimization is the key in the field of search algorithms, and there are various optimization methods used. One of such search algorithms is called Cuckoo search. Before getting into its dynamics and documentation, it is vital to understand the concept. Cuckoo Search is an algorithm, which is used for global optimization, on the basis of cuckoos’ behavior. In 2009, this search algorithm was developed by Suash Deb and Xin-she Yang. The idea is taken from bird species named cuckoo, and one of its practice is the basis of this search algorithm. It has been observed that Cuckoos used to lay their eggs, but in the nests, which are hosted by other birds. The situation can be good or bad for the Cuckoo, because if eggs are identified by the host that they do not belong to them, then they will throw these eggs, or they will leave their nest and will build a new one. But if eggs are not identified, then the host bird will hatch those eggs, and it will benefit Cuckoos. This method is inspired by the parasitic behavior shown by Cuckoo species (Mareli & Twala, 2018)

               It is important to know that Cuckoo search is also said to be a meta-heuristic optimization algorithm, and it is very effective in solving problems with regards to optimization. It was mentioned earlier that idea is taken from the activities of Cuckoo species, so it is a nature-inspired algorithm. So, this algorithm method is actually based on the reproduction strategy adopted by Cuckoos, and this strategy happens to be very aggressive as compared to other species. It is important to understand the Cuckoo breeding strategy in detail so that its breeding behavior can be understood, and then the discussion is made how this behavior is being utilized through the Cuckoo search algorithm. It would be quite interesting to know that some female Cuckoos have the capability to imitate patterns, as well as, colors of eggs, which are laid by some host species. This capability of Cuckoos allows them to keep their eggs safe because host species are not able to identify that these are not their own eggs. So, they are hatched, and the reproduction process continues without any problem. But this is not the case always, because some host species are good enough to discover that these are not their eggs, so they get into a conflict with intruding Cuckoos, and eggs are thrown away by them (Joshi, Kulkarni, Kakandikar, & Nandedkar, 2017)

               If one needs to understand, how Cuckoo search algorithm actually works, there here is the figure to show the mechanism of Cuckoo search:

   There are different strategies, which are associated with the Cuckoo search, and each category comes with its own mechanism, which had its own formulas and methods. There are various power features associated with Cuckoo Search, and one such features are called Lévy flights. The purpose of Lévy flights is to come up with new solutions, which means eggs. So, this is how it looks like when Mantegna’s algorithm is used to continue with production on the basis of Lévy flights:

In this formula, v and u are actually n-dimensional vectors. There is a certain normal distribution, which is used to calculate each element associated with v and u: and this distribution is given below:

            There is another strategy, which is based on the fact that some nests are replaced by constructing the new ones, which means new solutions are being found. In this method, a set of eggs is selected, and then they are replaced by the new solution. There will always be a probability associated with each egg. This method or operation can be understood by looking at the following figure with relevant values:

            The Cuckoo Search also comes with another strategy, which is called the Multimodal Cuckoo Search (MCS). It was mentioned earlier that Lévy flights are the most commonly used method in the Cuckoo Search so that new solutions are generated. There is a possibility that Cuckoo Search may not be able to provide a variety of solutions with regards to its single execution. In the MCS approach, it is proposed that Cuckoo Search should adopt multimodal capacities. There are three things, which can be achieved by doing so, and these three developments are quite important for making Multimodal Cuckoo Search (MCS). The division of 3 asymmetric states is used to implement the modification in Cuckoo Search so that MCS is achieved. The evolution process as per MCS is given below so that division can be analyzed accordingly:

            It is vital to know that Cuckoo Search can be used for findings solutions for a variety of problems, faced by different fields. For instance, a research study was conducted to see how IaaS cloud computing can use Cuckoo Search Algorithm to achieve Optimal Resource Scheduling. It is a fact that if cloud computing needs to be effective, then it should have optimal resource scheduling. In this study, a problem was identified with the IaaS cloud computing, so researchers wanted to find a solution to this problem with the help of Cuckoo Search. It is important to mention here that the study was also making a comparison amongst two algorithms, one is the existing ACO algorithm which is used with cloud computing, and the other one is the CS algorithm, being investigated to find the solution. Here is the flow chart, which is being used for the CS algorithm:

            After applying the CS algorithm and making its comparison with the existing ACO algorithm, it was found that the ACO algorithm was outperformed by the CS algorithm in all categories. It means that the CS algorithm was able to provide a great solution with better response time and scheduling for IaaS cloud computing. It also means that the CS algorithm can be used in a variety of such fields, where other algorithms have failed to provide any considerable solutions, so they should try CS algorithm to get better solutions and results. The better results as per the research for CS algorithm are shown below:

  It can be concluded in the end that there are so many dynamics and aspects associated with the Cuckoo Search Algorithm, and it is important for different fields to try this algorithm to find better solutions for their problems, as it has the capacity and mechanism to do so.

References of Cuckoo Search Algorithm

Cuevas, E., & Reyna-Orta, A. (2014). A Cuckoo Search Algorithm for Multimodal Optimization. The Scientific World Journal.

Joshi, A. S., Kulkarni, O., Kakandikar, G., & Nandedkar, V. m. (2017). Cuckoo Search Optimization- A Review. Materials today: proceedings, 4(8), 7262-7269.

Madni, H., Shafie, A. L., & Abdulhamid, S. M. (2017). Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithm. Sains Humanika, 9(1-3), 71-76.

Mareli, M., & Twala, B. (2018). An adaptive Cuckoo search algorithm for optimisation. Applied Computing and Informatics, 14(2), 107-115.