COA is a population-based technique introduced in [29]. The COA is one of the several recent and powerful metaheuristics. The first **algorithm** which is based on lifestyles of cuckoos was developed by Yang and Deb and is known as ‘**Cuckoo** Search’ (CS) [30]. The CS **algorithm** is based on the obligate blood parasitic behavior of some **cuckoo** species in combination with the Levy flight behavior of some birds and fruit flies. The CS **algorithm** does not completely imitate the behavior of cuckoos. It has not taken into account the immigration behavior of Cuckoos. Rajabioun developed another **algorithm** based on **cuckoo** lifestyle, called “**Cuckoo** **Optimization** **Algorithm**” (COA) [29]. This **algorithm** inspires and models **cuckoo** life cycle much better and more precisely. He proved the efficiency of this **algorithm** via a benchmarking study. He showed that this **algorithm** has high speed of convergence and reaches the global solution easily. COA is superior than other nature inspired computing algorithms because of multiple functions of COA operators (including egg laying and immigration operators). Other **optimization** algorithms have the operators which are defined for one specific objective. The performance of COA on benchmark functions and its features tempted the authors to apply the **algorithm** to linear and circular antennas as to see how it performs for antenna problems.

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3. 1. **Cuckoo** **Optimization** **Algorithm** **Cuckoo** **Optimization** **Algorithm** (COA) has been inspired by the life of a bird, called **cuckoo** [16]. The initial population of COA which forms various societies, consists of cuckoos and eggs. Each **cuckoo** has some eggs and also an Egg Laying Radius (ELR). The cuckoos lay eggs inside their equivalent ELR and in the nests of other host birds. Among all the eggs, those ones, which are similar to the eggs of the host birds can grow up. The rate of grown eggs indicates the suitability of the area. The area with more remained eggs has higher profit. Cuckoos always search for areas with highest profit for egg laying. Therefore, selecting the best place is an important term which should be optimized by the cuckoos. The cuckoos which live in the worst habitats always are removed. Each **cuckoo** travels a specific percent of the whole path toward the ideal habitat with a clarified deviation which are known as and respectively. These two parameters help the cuckoos to find the ideal habitat. The maximum number of cuckoos should be confined in the specific environment. In fact, cuckoos have been clustered and the best habitat is detected to achieve the objective point. Consequently, the new **cuckoo** population can travel to the objective habitat. Now, the survival of eggs in the nest are checked and the profit value is obtained. A suitable profit value can lead to stopping the process. Otherwise, the whole process should start from the beginning in accordance to the flowchart, presented by Amiri and Mahmoudi [16]. In fact, the survival process of cuckoos should finally converge to a condition with only one **cuckoo** society, containing the same profit values.

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Initially preprocess the original database to reduce the time consumption and avoid the production of useless and unrelated solutions for association rule hiding. The pre-processing is carried out in two phases. The original database is processed in the first phase where only acute transactions of the database are selected. In the second phase, only the critical sensitive items which need sanitization are altered. After preprocessing and selection of minimum number of alterations, a **Cuckoo** **Optimization** **Algorithm** for Association Rule Hiding (COA4ARH) process is in progress with modifying the population of **cuckoo** N pop .

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Computational grid is a hardware and software infrastructure that provides dependable, inclusive and credible to other computing capabilities. Grid computing intercommunicated with a set of computational resources on a large scale. Scheduling independent jobs is an important issues in such areas as computational grid. Scheduling is the process of assigning jobs to resources in order to achieve different goals. The grid schedule, find the optimal resource allocation to it over heterogeneous resources and maximize overall system performance. As yet evolutionary methods such as Genetic, Simulated Annealing (SA), Particle Swarm **Optimization** (PSO) and Ant Colony **Optimization** (ACO) to solve the problem in the grid schedule has been adopted. The disadvantage of these techniques premature convergence and trapping in local optimum in large-scale problems. In this paper, a method by **Cuckoo** **Optimization** **Algorithm** (COA) to solve job scheduling in grids computational design, implementation and results are presented. The results show our proposed schedule have more efficient and better performing compared with Genetic and Particle Swarm **Optimization**.

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For assessing and selecting sustainable suppliers, this study considers a triple-bottom-line approach, including profit, people and planet, and regards business operations, environmental effects along with social responsibilities of the suppliers. Diverse metrics are acquainted with measure execution in these three issues. This study builds up a new hybrid intelligent model, namely COA-LS-SVM, for taking performance variations of the sustainable suppliers quantified by the performance index. The presented artificial intelligent (AI) model is introduced in light of a new combination of least squares-support vector machine (LS-SVM) and **cuckoo** **optimization** **algorithm** (COA). The LS-SVM is used in regards to the mapping capacity amongst performance index and its causative input criteria. The COA is presented to advance LS-SVM tuning parameters. In this exploration, an illustrative database comprising of 80 historical cases is gathered to set up the presented intelligence system. In the light of experimental results, the presented COA-LS-SVM can effectively illustrate performance index’s variances since it has accomplished relatively low statistical metrics. Therefore, the proposed hybrid AI framework can be a promising approach to help the supply chain decision-makers in sustainable supply chain management (SSCM).

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Forecasting changes in level of the reservoir are important in Construction, design and estimate the volume of reservoirs and also in managing of supplying water. In this study, we have used different models such as Artificial Neutral Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) and **Cuckoo** **Optimization** **Algorithm** (COA) for forecasting fluctuations in water level of Chahnimeh reservoirs in south-east of Iran. For this purpose, we applied three most important variables in water levels of the reservoir including evaporation, wind speed and daily temperature average to prepare the best entering variables for models. In addition, none accuracy of error in estimation of hydrologic variables and none assurance of exiting models are the result of their sensitivity to the educational complex for teaching of models and also preliminary decoration before beginning general education has been estimated. After comparing exiting and confidence interval of the ANN and ANFIS has been found that the result of ANFIS model is better described than other model because it was more accurate and does have lesser assurance.

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Task scheduling in cloud computing is a complex problem. As it is clear, load balancing in clouds is a NP-Complete problem and gradient-based methods which search for an optimal solution to NP-Complete problems cannot converge to the best solution in an appropriate time. Therefore, in order to solve load balancing problem, evolutionary and meta-heuristic methods should be used. Thus, in this study, in order to find a solution for load balancing in cloud computing, **Cuckoo** **Optimization** **Algorithm** (COA) is used and it is compared with other methods including evolutionary and non-evolutionary algorithms. In order to prove efficiency of the method, COA is presented and simulated in Cloud-Sim simulator. Obtained results are better than results of GA and Round- Robin scheduling. Finally, it is found that the leader presented in this study gives more optimal outputs in heterogeneous (cloud) environments and user’s request is processed in an acceptable time. Thus, agreement is achieved at service level and user’s satisfaction is increased.

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Abstract: The notion of enhancement of the image is to ameliorate the perceptibility of information contained in an image. In the present research, a novel technique for the enhancement of image quality is propounded using fuzzy logic technique with a **cuckoo** **optimization** **algorithm**. Generally, the image is transformed from RGB domain to HSV domain keeping the color information intact within the image. The image has been categorized into three regions: underexposed, overexposed and mixed region on the basis of two threshold values. For the fuzzification of under and overexposed area the degree of membership is defined by the Gaussian membership, while the mixed area is fuzzified by parametric sigmoid function. The key parameters like visual factors and fuzzy contrast provide the quantitative analysis of an image. An objective function is framed which involves entropy and visual factor has been optimized by a new evolutionary **cuckoo** **optimization** **algorithm**. The results procured after simulation by the **cuckoo** **optimization** **algorithm** are compared with Bacterial foraging **algorithm** and ant colony **optimization** based image enhancement and this approach is found to be improved.

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To increase the reliability of the ordinary three phase wind power system, six phase wind power system is proposed [14]. Beside their reliability, poly-phase electrical machines have many advantages compared to three phase machines, including lower total harmonic, reduced amplitude and increased frequency of pulsating torque and lower current per phase for the same rated voltage [15]. A direct-drive wind power generator has to operate at very low speeds. According to the electric machine design principles, these types of machine are characterized by the large dimensions and weight [5], [11]. The design of low-speed, large-dimension generators has to be optimized in terms of cost, total volume, and efficiency. In order to minimizing the generator losses, maximizing efficiency and improve the thermal characteristic of the generator, the generator losses is chosen as one of the objective functions. To reduce the total volume, weight and cost of generator and wind turbine structure, total volume and manufacturing cost of PMSG are chosen as other objective functions. Therefore, single/multi-objective optimizations have been done to find the best design for six-phase PMSG for direct-drive wind power. For this purpose, the equations needed for PMSG design are extracted and then the COA is used to optimal design. The rest of this paper is organized as follows. The main equations needed for PMSG design are presented in Section 2. Generator losses are introduced in Section 3. In Section 4, cost estimation for PMSG manufacturing is presented. Wind turbine modeling and calculating shaft speed are presented in Section 5. In Section 6, **cuckoo** **optimization** **algorithm** is introduced. The design procedure is expressed in Section 7. Generator parameters and design variables are given in Section 8. In Section 9, optimal design is carried out. In

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In Fig. 3, the **Cuckoo** **optimization** **algorithm** is plotted. Like other evolutionary COA algorithms, it starts with an initial population. A population of cuckoos, this population of eggs that will lay them in a host of birds. Other eggs are identified and killed by the host bird. The amount of eggs hatched indicates the suitability of the locusts in the area. The more eggs in a given area are able to survive and survive, the greater the benefit to

21. M. Yakhchi, S.M. Ghafari, S. Yakhchi, M. Fazeliy, A. Patooghi, “Proposing a Load Balancing Method Based on **Cuckoo** **Optimization** **Algorithm** for Energy Management in Cloud Computing Infrastructures”, Proceedings of the 6th International Conference on Modelling, Simulation, and Applied **Optimization** (ICMSAO), 2015. 22. D.Powar, S. S. Moharana, R. D. Ramesh “analysis of load

To furthermore attest efficiency of ADCS approach, 100 images of Berkeley database and images of mechanical objects are used in this experience, keeping the same experimental setup described above. Results obtained using the approach proposed in this work, the novel Adaptive Discrete Cukoo Search **Algorithm**, are confronted to different approaches established in our previous works as discrete particle swarm **optimization** (DPSO) [34], ant colony **optimization** (ACO) [17], and another approach in literature such as reinforcement learning (RL) [6]. Fig. 14 shows the error rates obtained by the proposed ADCS approach in contrast to other techniques presented above, just 30 images are presented for the plot clarity. The error average for the 100 images using ADCS approach is 0,14x10 -2 when the average error for DPSO approach is 0,17x10 -2 , for ACO approach the averange error of 100

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Kaur and Gill [17] proposed CSA for solving short-term fixed-head hydrothermal scheduling (HTS) problem taking into consideration, both the power losses in transmission systems and valve point loading effects in fuel cost function of thermal units. The proposed approach was tested on 4 and 5 hydrothermal systems using different fuel cost functions of thermal units. MATLAB platform was used as implementation tool and ran on a 2 GHz PC with 2 GB of RAM. The work emphasized that different values of the probability led to the same optimal solution and that the best value of probability of alien egg being discovered (Pa) had to be tuned in its range [0, 1]. The work suggested that value of distribution factor should be in the range [0.3, 1.99] as it had significant impact on solution quality of CSA. The results obtained were compared to optimal gamma based genetic **algorithm** (OGB-GA), existing GA (EGA), artificial immune system (AIS), EP, PSO and DE. It was concluded based on the analysis that the proposed approach was a favorable method for solving the short-term hydrothermal scheduling problem, especially for non-smooth fuel cost function of thermal units.

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CS is also nature inspired **algorithm**. Cuckoos are extremely interesting birds because they lay their eggs in the nests of small songbirds whose eggs are also looking same. They lay their eggs either just before the host bird lays or just after it, so that host bird can‟t identify these. If the host bird identifies that the eggs are of another bird than the host bird it destroys these or build a new nest, if not then eggs grow up with host eggs. They lay their eggs same as host bird eggs. As first egg become chick, it pushes the host eggs outside the nest and share the host bird food. According to some studies **cuckoo** chicks can also copy their call same as call of host chicks to get opportunity of feeding. Because they lay their eggs into another nest, that‟s why they are also called parasitism. Intraspecific blood parasitisms are those birds that destroy the host bird eggs.

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Abstract - The **optimization** problems that are encountered are solved by implementing some metaheuristic techniques. In this paper, two of the meta-heuristic techniques are undertaken for checking the optimality of the set of solutions. One of the techniques is **cuckoo** search i.e. able to generate the set of test cases and optimizes them. Another **algorithm** is firefly **algorithm** is used here for checking the optimality of set of test cases that are generated from the **cuckoo** search. The paper reviews the concepts of test case generation and **optimization** and produces the results of maximum code coverage in the execution of optimal set of solutions

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Differential Evolution (DE) **algorithm** is an evolutionary **algorithm** based on population differences, and an intermediate population can be got through recombination according to the difference of population. Then, the offspring is generated by competition between the current population and the intermediate population. The most marked characteristic of DE **algorithm** is mutation operator, and it generates the offspring by the difference of two random weighted individuals after one random individual is selected. The mutation operator is added to the **algorithm** which reinforces its search ability. In early iterations, the individuals in the population are quite different, and the global search of the **algorithm** is enhanced by mutation operator; however, the diversity of the population decreases and the **algorithm** converges gradually with more and more iterations. The local search of the **algorithm** is enhanced by mutation operator. The individuals of the developing sub-population are used to help jumping out of local optimum by reference to mutation operation, **algorithm** global convergence is guaranteed. The formula of mutation operation is as follows:

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B. Radojevic[12] has considered to fix the load balancing algorithms and overcome the defects round robin **algorithm**, Which is the very famous **algorithm** and it is working on the basis of a conversion session in the application layer. The main feature of the **algorithm** is to improve communication time between the customer and the node in the cloud computing. If the connection time has exceeded the threshold standard, then the relation between the customer and the node will be finished, the task is to provide some other node by using round robin rules. The approach sends a request to a node with less number of communications.

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Abstract: Brain tumor is an unusual intensification of cells inside the skull. The brain MRI scanned images is segmented to extract brain tumor to analyze type and depth of tumor. In order to reduce the time consumption of brain tumor extraction, an automatic method for detection of brain tumor is highly recommended. Deep machine learning methods are used for automatic detection of the brain tumor in soft tissues at an early stage which involves the following stages namely: image pre-processing, clustering and **optimization**. This paper addresses previously adduced pre-processing (Skull stripping, Contrast stretching, clustering (k-Means, Fuzzy c-means) and **optimization** (**Cuckoo** search **optimization**, Artificial Bee Colony **optimization**) strategies for abnormal brain tumor detection from MRI brain images. Performance evaluation is done based on computational time of clustering output and **optimization** algorithms are analyzed in terms of sensitivity, specificity, and accuracy.

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Viswanathan and some other researchers proved that the albatross adopts the pattern of Lévy flight for foraging. By using satellite positioning system they discovered their flying intervals follow power-law distribution, explained their findings through the space distribution property of invariable food scale on the sea surface and published some symbolic papers [19-21]. After having researched the foraging path of bees [22] and drosophilas [23], Reynolds discovered the appearing occurrence of the straight line portion in their flight path corresponds with scale-free inverse square of lévy distribution in that they both take on lévy flight properties. When the target positions are distributed randomly and sparsely, lévy flight is the ideal search strategy to M independent explorers [24]. In addition, lévy flight is found in creatures such as spider monkeys, gray seals and reindeer as well in human being’s behavior [25,26]. Lévy flight is a sort of random walk. The step size meets a heavy-tailed stable distribution. Short-distance exploration and occasional long-distance walk interphase in it. Lévy flight used in intelligent **optimization** **algorithm** can enlarge search area, increase population diversity and jump out of local optimal point easily [27].

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