cuckoo optimization algorithm

Cloud computing provides computational services such as servers, memory, storage space, databases, networks, software, analytics, and information as virtualized resources through the internet to offer faster innovation, flexible resources, and cost savings at scale. Although cloud computing service providers are innovatively expanding their services, trust is one of the major obstacles to the progress of this matter. Trust is the biggest issue in cloud computing since trust is an effective guarantor during interactions between the users and the providers. Trust is one of the most fundamental methods for increasing confidence in resources provided in the cloud environment and is important in cloud business environments. With the increasing number of cloud services providers in the cloud computing environment and the number of users, the selection of provider has become a major challenge. The Coa algorithm has a higher convergence speed, at least by 5.9%, compared to the studied algorithms. In this research, an optimization approach based on a metaheuristic process using the COA algorithm combined with the K-means clustering algorithm is proposed to solve the optimization problem of selecting the best provider in the trust management third-party component layer based on parameters. In this method, while reducing trust evaluation time, the accuracy of user access to the most trusted provider based on user priorities has increased compared to previous methods. This can increase user confidence and improve the quality of service providers.

Face recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge that has been the focus of various researchers in recent years. The feature extraction process and classification are two important issues in diagnosis systems that can play a significant role in increasing the accuracy of diagnosis. Considering this issue, in this study, taking into account the combined features and optimizing the cuckoo algorithm, a method to improve the accuracy of face recognition is proposed. In the presented method, seven features are extracted from the images in the database, and then by obtaining the feature vector, the steps related to feature selection are performed using the cuckoo algorithm. The proposed method has been implemented with MATLAB software and compared with other methods. The evaluation results show that the proposed method was able to perform the detection on the images of ORL and FDBB databases with 93.00% and 95.12% accuracy, respectively. The result obtained for this evaluation criterion has a higher value than other compared methods.

Resource leveling is very important in projects and project managers always need a schedule based on the optimal use of resources needed to complete their projects. Most resource leveling research has been done in a single project, while in many organizations several projects are done simultaneously. For this purpose, a mathematical model is presented by all projects with the aim of minimizing changes in the level of different resources. Leveling and allocating resources is one of the most basic tasks of project management. Typically, project management uses specific methods such as GERT and PERT to plan and control the project (Boctor, 1990). Because the problem of resource leveling is an NP-complete problem and it is not possible to achieve the optimal solution in the general case (Guo et al., 2012), the cuckoo optimization algorithm has been used, which is one of the newest and most efficient evolutionary optimization methods.

During recent years, increment of incentives for deliberate subversive activities against power systems along with the restrictions on the allocation of financial resources for protection of these infrastructures have absorbed significant attentions of researchers toward the necessity of optimal allocation of these finances; and such the way, to minimize damages and consequently energy not supplied in case of future events. Planners of subversive activities as fully strategic actors target those low probability events which are no considered in protection schemes to maximize quantity of damages to power system. In this study, a new game theory based scheme is proposed in form of a zero sum playoff in order to provide an optimal allocation strategy. In this paper, two budget allocation algorithms have been presented to protect transmission lines and substations against deliberate threats. In the first algorithm, allocation of a certain amount of annual funding to transmission lines and substations is formulated with the aim of attaining the best possible condition of power grid in term of system reliability; while, required budget and procedure of allocation of this budget to targeted system utilities against undefined strategy of malicious individuals are calculated in second algorithm, in order to reach a predefined level of system reliability. Proposed model is implemented employing MATLAB (i.e. to execute single-objective Cuckoo optimization algorithm as well as multi-objective non-dominated sorting genetic algorithm II) and GAMS (i.e. to determine reliability of grid by use of power system load flow) software. Totally, optimized game strategy of protectors to acquire the best condition of system reliability has been obtained. Results validate effectiveness and applicability of proposed method in cases of optimality of allocation technique and subsequently increase of reliability indices.

One of the simplest and most commonly used control methods for using in automatic voltage regulators (AVR) is the PID controller. On the other hand, Fractional calculus has recently been proposed as a powerful tool in the modeling and control of dynamic systems in control engineering, whose abilities and applications of this theory are being studied in academic research. In the control of dynamic systems, fractional-order controllers can improve the performance and control capabilities of the system. In this paper, a multi-objective approach to designing an optimal fractional PID controller for regulating the output voltage of the synchronous generator is investigated using cuckoo optimization algorithm. The proposed cost function includes rise time, settling time, steady state error and overshoot. The comparative results obtained show that the fractional PID controller has a higher robustness to parametric uncertainties and disturbance than the classic PID controller. The comparative results obtained show that the fractional PID controller has a higher robustness to parametric uncertainties and disturbance than the classic PID controller.

Over product oriented course to industry one, firms' competitiveness is being complicated to gain more portion of market that lead to dynamic and more variation environment. In this situation, customers find more authority to select their favorite products and services. Response to market fluctuation to supply customers' needs is considered as an important tool to firms' promotion. Needs to reduce costs and improve organization process cause to pay more attention to supply chain concept. The main goal of each supply chain satisfies customers' needs with the lowest cost and highest efficiency.Structurally, supply chain includes retailor, wholesaler, distributor, manufacturer, and supplier. An efficient logistic network should be designed so that response to uncertainty. Since time and cost are the most important factors in reverse logistic design, a fuzzy two objectives optimization model is proposed in this study. First, a fuzzy mathematical programming model presented. The aim of this model is determining delivery goods amount among centers so that total cost and total delay time are minimized. In this research is used fuzzy approach to cover uncertainty in reverse logistic network. A numerical example has been produced and solved by GAMS. In order to solve the problem in large scale a cuckoo optimization meta heuristic algorithm is developed. The results indicate that the total amount sent to the manufacturer of the values obtained from the exact solution and the objective function value decreases with increasing number of iterations which this indicates proper / valid operation is the proposed algorithm.

In today's world, increasing water needs because of increasing in human population and the advancement of industry and agriculture is inevitable. On the one hand the restrictions and the principle of sustainability in the management of water resources, water supply and all the existing requirements made it impossible. To understand systematic approaching and integrated management of water resources allocation need to know the rules of programming. This method can estimate how to achieve the goals of water resources management. According to the characteristics of different methods used in water resources planning topics, can be concluded that the effective optimization of process issues of water resources management at basin level requires the combination use of a comprehensive simulation model with capability of fixing details at the basin scale and an optimization algorithm with capability of having many decision variables, and appropriate speed to the convergence response of optimization problem and also the lack of need to change the current relationships and equations on the question form. The factors enumerated show the necessity of doing this research, in this regard, the optimum utilization of the water system's tank with the optimum allocation on the basin with the combination of MODSIM model as a simulation model and COA (Cuckoo Optimization Algorithm) as an optimum algorithm, were reviewed. Feature of MODSIM model in direct contact with the COA algorithm can be outlined for this remarkable method.

Nowadays with the rapid rate of urban development and increasing volume of vehicles and traffic restrictions, routing in urban networks is not only necessary but essential. Management of such massive volume of data makes the need to for GIS with capabilities to conduct spatial data analysis inevitable.
People often, when deciding to start a journey from one location to another, consider not only which route and means of transportation will save them time, but also which are the most inexpensive and cost effective. Hence, they outline the issue as a question in their mind, and based on the criteria, seek to find the optimal solution. The same behavior occurs in a different routing system. Finding the most optimal, efficient and shortest route is one of the key pillars in route finding for which finding the right solutions could lead to answering other questions on the issue. In fact, for a more in depth level of analysis, the answer to this question is essential; Finding the shortest path possible from a starting point or origin, to an ending point or destination. Metaheuristic algorithms are estimating algorithms, that are able to find optimal or almost optimal solutions in a reasonable time.
The showcased methodology in this research for solving the optimal route is recommended for the first time and is the Cuckoo Optimization Algorithm. The reason for choosing this algorithm, is the fact that it is a new method that provides appropriate solutions for different problems than other meta-heuristic algorithms. Route finding which is by nature a discrete problem, is managed by changes in binary version of this algorithm. In setting up the first population, a controlled approach was used to prevent the creation of random populations, that only a few of them could create routes. In this method, population variables that are basically the same network points and situations of each cuckoo are not randomly selected. These variables are selected in a controlled system. Meaning, selection of each next node is from those that are connected to it. While implementation of the algorithm, cuckoo’s locations are converted to binary numbers, if a node exists in the route it will become 1 and if not 0. A Sigmoid Function is used in the migration phase of the Cuckoo. In this phase the new location of Cuckoo stands between the range of zero and one, and other locations are converted to zero and one. To test the recommended algorithm, three network are used; hypothetical, local and real networks. The result of running this algorithm in 2 hypothetical and local networks with 20 and 31 nodes was the same result of a deterministic algorithm. However, in a network, that was part of a real network and composed of 617 nodes and 995 arcs, it could indicate the optimal route slightly better than that of deterministic algorithm. The results showed that the algorithm is capable of routing in the network and with some changes on the structure of the network can be used on networks with large data.