cuckoo search algorithm

Pharmaceutical companies face complex challenges in designing and managing their supply chains due to regulatory requirements at the national, international, and internal levels, along with government-imposed constraints such as the sourcing of raw materials, distribution, exchange rates, and production and storage conditions. Designing green and closed-loop supply chain networks can play a crucial role in reducing costs, increasing efficiency, and minimizing the environmental impact of the industry. In this paper, a model for designing green and closed-loop supply chains for pharmaceutical products is proposed, focusing on the optimal location of production, distribution, and recycling centers. The proposed model takes into account both internal factors, such as the selection of raw materials and green technologies, and external factors like optimal site selection and transportation system optimization. The goal of this model is to minimize fixed and operational costs, reduce environmental pollutant emissions, and enhance supply chain sustainability. To solve the problem, mathematical modeling was applied using GAMS software, and the Cuckoo Search Algorithm (CSA) was implemented in MATLAB software. This approach provides optimal and efficient solutions for complex problems, yielding reliable results. The findings of this research indicate that the proposed approach can play a significant role in improving performance and greening pharmaceutical supply chains.

In this paper, an electrical equivalent circuit model based on the photovoltaic effect has been presented with the studies done on the simulation of the solar energy system. This linear model, which consists of two diodes, shows the behavior of a solar cell to produce electricity. We have done the desired simulations using MATLAB software. Our goal in this research is to calculate the minimum error value for the unknown parameters of the circuit, which is obtained by the root-mean-square error (RMSE). In order to accurately and reliably determine the parameters of the double-diode model, an optimization method based on collective intelligence called the Cuckoo search algorithm is presented in this article. According to the desired model that we intend to study with the proposed algorithm, to obtain the minimum error value, we calculate the unknown parameters of the circuit and compare them with other methods. The results show that the RMSE value of the proposed algorithm with the initial population value of 50 and the number of iteration rounds of 1000 is equal to 3.56*10-2, which provides better results than other algorithms. The average execution time of this algorithm is 15.81 milliseconds per every iteration round.

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of different techniques for time series missing data imputation, which usually include simple analytic methods and modeling in specific applications or univariate time series. In this paper, a hybrid approach to obtain missing data is proposed. An improved version of inverse distance weighting (IDW) interpolation is used to missing data imputation. The IDW interpolation method has two major limitations: 1) finding closest points to missing data 2) Choosing the optimal effect power for missing data neighbors. Clustering has been used to remove the first constraint and find closest points to the missing data. With the help of clustering, the search radius and the number of input points that are supposed to be used in interpolation calculations are limited and controlled, and it is possible to determine which points are used to determine the value of a missing data.Therefore, most similar data to the missing data are found. In this paper, the k-maens clustering method is used to find similar data. This method has been more accurate than other clustering methods in multivariate time series. Evolutionary algorithms are used to find the optimal effect power of each data point to remove the second constraint. Considering that each sample within each cluster has a different effect on the estimation of missing data, cuckoo search is used to find the effect on missing data. The cuckoo search algorithm is applied to the data of each cluster, and each data sample that has more similarity with the missing data has more influence, and each data sample that has less similarity has less influence and has less influence in determining the amount of missing data. Among evolutionary algorithms, evolutionary cuckoo search algorithm is used due to high convergence speed, much less probability of being trapped in local optimal points, and ability to quickly solve high dimensional optimization problems in multivariate time series problems. To evaluate the performance of the proposed method, RMS, MAE, , MSE and MAPE criteria are used. Experimental results are investigated on four UCI datasets with different percentages of missingness and in general, the proposed algorithm performs better than the other three comparative methods with an average RMSE error of 0.05, MAE error of 0.04, MSE error of 0.003, and MAPE error of 5. The correlation between the actual data and the estimated value in the proposed method is about 99%.

Multi-cloud environments consist of the considerable variety of resources where the cost of scheduling workflow applications can be significantly reduced in such environments and the resource limitationsimposed by commercial cloud providers can bealso overcome. Accordingly, this study addresses the scheduling of scientific workflowapplications in a multi-cloud environment under a deadline with the aim of minimizing costs. In this paper,an algorithm for scheduling of workflow applications in multi-cloud environment is presented using the cuckoo search algorithm which is one of the most popular meta-heuristic methods. The Cuckoo Search Algorithm is able to search the solution space in a short time and find solutions in the vicinity of the optimal global solution that is close to it. The results show that the proposed approach of this research has better performance in comparison with other meta- heuristic approach in terms of cost reduction. Moreover, the obtained solutions of the proposed meta- heuristic algorithm are in a desirable degree close to the global optimal solutions of mathematical model.

The current demand in the power system has led to increased usage of the Distributed Generation (DG) and renewable resources. The renewable resources can efficiently supply the loads in the micro grids. The output power generation of renewable energy resources is unpredictable. Hence, in this paper the optimal generation dispatch of the DGs in micro grids in both grid-connected and islanded modes is determined. Since the operation of the micro grid in presence of DGs and various constraints is a complicated optimization problem, in this paper a meta-heuristic Cuckoo search (CS) algorithm with high convergence speed is used. In order to reduce the uncertainty of the output power of photovoltaic system the energy storage system is implemented and the optimal capacity of the storage is determined based on operation conditions. The proposed algorithm for determining the optimal capacity of the battery in a sample micro grid is applied. The results show the effectiveness of the proposed method for determining the optimal dispatch of the DGs and capacity of the energy storage system.

Considering the high complexity of scheduling problems, classic approaches fail to find the solution efficiently. Therefore, meta-heuristic algorithms are used for this purpose. In this paper, Cuckoo optimization algorithm (COA) is used as one of the novel and most effective evolutionary optimization algorithms for flexible job shop scheduling. In the proposed approach, for better solutions, the initial population is determined using NEH-D algorithm, which considers the completion time minimization of each job. Then active machines are grouped using Markov clustering, so that the assigned machine is chosen from the active ones, hoping that the possible solutions of COA are bounded and the execution speed of the algorithm is increased. Finally, COA is applied for job-machine assignment and Levy flight is used to improve the global search of the algorithm. The proposed approach is evaluated on standard datasets such as Kacem, Brandimarte and other related data. The experimental results show that the proposed algorithm is capable of finding the final solution with lower computational complexity and has higher convergence rate.

Heterogeneous wireless sensor network consists of several nodes with different functions and characters. Energy minimizing in these networks is one of the challenging issues. Several approaches have been presented to solve this problem up to now; such as Genetic Algorithm, Bee Algorithm, PSO Algorithm and etc. In this paper a novel meta-heuristic algorithm Cuckoo Search Algorithm (CSA), is introduced to solve this problem. The main contribution of this paper is to reach an optimum trade-off between the number of super nodes and network efficiency to decrease the energy consumption. MATLAB, simulation toolkit, is used to simulate the efficiency of this method. Simulation results show that proposed algorithm quickly finds a good solution and has better performance than Genetic based algorithm.