Precedence constrained sequencing problem (PCSP) is related to locate the optimal sequence with the shortest traveling time among all feasible sequences. In PCSP, precedence relations determine sequence of traveling between any two nodes. Various methods and Algorithms for effectively solving the PCSP have been suggested. In this paper we propose a Cuckoo Search Algorithm ((CSA)) for effectively solving PCSP. (CSA) is inspired by the life of a bird named Cuckoo. As basic (CSA) at first was introduced to solve continuous optimization problem, in this paper to find the optimal sequence of the PCSP, some schemes are proposed with modifications in operators of the basic (CSA) to solve discrete precedence constrained sequencing problem. To evaluate the performance of proposed Algorithm, several instances with different sizes from the literature are tested in this paper. Computational results show the good performance of the proposed Algorithm in comparison with the best results of the literature.

Today, with rapid growth of the World Wide Web and creation of Internet sites and online text resources, text summarization issue is highly attended by various reSearchers. Extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. When, we are facing into large data volume documents, the extractive-based text summarization seems to be an unsolvable problem. Therefore, to deal with such problems, meta-heuristic techniques are applied as a solution. In this paper, we used Cuckoo Search Optimization Algorithm (CSOA) to improve performance of extractive-based summarization method. The proposed approach is examined on Doc. 2002 standard documents and analyzed by Rouge evaluation software. The obtained results indicate better performance of proposed method compared with other similar techniques.

The efficient operation of electrical distribution systems is critical in modern industry, as it directly impacts the reliability, stability, and cost-effectiveness of delivering electricity to consumers. Capacitors are included in radial distribution systems to improve voltage profile and minimize losses by providing reactive power. Consequently, losses are reduced as the reactive power flow component is compensated. Furthermore, re-configuration of the distribution network, which involves altering the open/closed status of switches, is a vital approach that affects the steady flow of electricity through the network. Network reconfiguration and optimal capacitor placement are essential techniques for enhancing the performance of the distribution networks. This study utilizes the Cuckoo Search Algorithm ((CSA)) to solve the problems of network reconfiguration and optimal capacitor placement. The primary objective is to minimize power losses while ensuring that the voltage profile and reliability of the distribution system satisfy industry-level standards. The proposed method was tested on the IEEE 33 bus network. Five different scenarios were considered. The simulations were conducted in MATLAB software. The acquired improvements in the power loss reduction and voltage profile corroborate the effectiveness of this novel approach. Compared to previously explored methods, the results of the proposed scheme for solving optimal capacitor placement and network reconfiguration individually were found to be more effective in terms of reducing power loss (3.12% and 4.02%, respectively).

In this paper optimum design of truss structures for both discrete and continuous variables based on the Cuckoo Search (CS) Algorithm is presented. The CS is one of the recently developed population based Algorithms inspired by the behavior of some Cuckoo species together with the Levy flight behavior of some birds and fruit flies. In order to demonstrate the effectiveness and robustness of the present method, minimum weight design of truss structures is performed and the results of the CS and the selected well-known meta-heuristic Search Algorithms are compared for both discrete and continuous design of three benchmark truss structures.

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.

Different kinds of meta-heuristic Algorithms have been recently utilized to overcome the complex nature of optimum design of structures. In this paper, an integrated optimization procedure with the objective of minimizing the self-weight of real size structures is simply performed interfacing SAP2000 and MATLABR softwares in the form of parallel computing. The meta-heuristic Algorithm chosen here is Cuckoo Search (CS) recently developed as a type of population based Algorithm inspired by the behavior of some Cuckoo species in combination with the Levy flight behavior. The CS Algorithm performs suitable selection of sections from the American Institute of Steel Construction (AISC) wide-flange (W) shapes list. Strength constraints of the AISC load and resistance factor design specification, geometric limitations and displacement constraints are imposed on frames. Effective time-saving procedure using simple parallel computing, as well as utilizing reliable analysis and design tool are also some new features of the present study. The results show that the proposed method is effective in optimizing practical structures.

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.

Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer reSearches, having a few samples of microarrays in cancer reSearches is one of the most concerns which lead to some problems in designing the classifiers. Moreover, due to the large number of features in microarrays, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Hence, appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, a modified multi-objective Cuckoo Search Algorithm is used to feature selection and sample selection to find the best available solutions. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original Algorithm. The proposed Algorithm is applied on six cancer datasets and its results are compared with other existing methods. The results show that the proposed method has higher accuracy and validity in comparison to other existing approaches and is able to select the small subset of informative genes in order to increase the classification accuracy.

In this study, an efficient logistics network was designed to optimize both time and cost as the most effective factors using a mathematical model (two-objective fuzzy optimization) in a reverse logistics system. This paper attempted to determine the value of goods sent between return processing centers in any period of time in order to minimize the total cost and time of delay within supply chain. The fuzzy approach was adopted in order to consider uncertainty in reverse logistics network. The validity of model was measured through a model proposed by Azar Resin Chemical Industrial Company and then implemented and solved by GAMS software. According to the previous studies that implemented the model at a smaller scale, the problem revolved around designing NP-hard logistics network. Hence, exact methods cannot solve these problems on a large scale. Therefore, for solving the problem, Meta-Heuristic Algorithms was used in this study. Because Cuckoo Search Algorithm has a high efficiency in comparison to other Algorithms. In order to validate the newly proposed Algorithm, the results were compared against the exact solution. The findings suggested that the proposed Cuckoo Algorithm was sufficiently accurate to solve the problem and achieve values similar to exact solution.

This paper presents a damage detection and localization based on vibration analysis in beam using Cuckoo Search Algorithm. The damage is represented by a reduction in Young’s modulus. The finite element method is used to apply damage at specific element(s) of the considered beams. The identification of damage is formulated as an optimization problem using objective function based on changes in natural frequencies. A procedure for detecting and locating damage of H-beam with thin plates structures based on Cuckoo Search Algorithm is used. This approach presents a method that can be used to detect the single and multiple-damage positions and the rate of damage in structural elements with high accuracy after the first iteration. The noise introduced in the damage problem, it is shown that our approach based on Cuckoo Search Algorithm can detect the damage with high accuracy. Making a comparative study of the obtained results with three well-known optimization Algorithms confirms that the proposed Cuckoo Search Algorithm produces better performance in damage detection and localization based on vibration analysis and optimization in beam.