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.

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.

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 is concerned with the economical comparison between two commonly used configurations for double layer grids and determining their optimum span-depth ratio. Two ranges of spans as small and big sizes with certain bays of equal length in two directions and various types of element grouping are considered for each type of square grids. In order to carry out a precise comparison between different systems, optimum design procedure based on the CUCKOO SEARCH (CS) ALGORITHM is developed. The CS is a meta-heuristic ALGORITHM recently developed that is inspired by the behavior of some CUCKOO species in combination with the Levy flight behavior of some birds and insects. The design ALGORITHM obtains minimum weight grid through appropriate selection of tube sections available in AISC Load and Resistance Factor Design (LRFD). Strength constraints of AISC-LRFD specification and displacement constraints are imposed on grids. The comparison is aimed at finding the depth at which each of the different configurations shows its advantages. The results are graphically presented from which the optimum depth can easily be estimated for each type, while the influence of element grouping can also be realized at the same time.

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).

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.

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 RoundRobin 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.

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.

Choosing the right portfolio is a crucial goal for investors. Two key factors to consider when selecting a portfolio are return and portfolio risk. This problem can be written as a mathematical programming equation and can be solved quickly. For instance, some investors choose stocks based on past performance, while others consider factors like liquidity when making their selections. Additionally, fundamental and technical analysis are often used in stock and portfolio selection. However, the strategies and methods used to select stocks and portfolios can vary depending on the current market conditions and the investor's level of knowledge. This article focuses on designing and explaining a portfolio OPTIMIZATION model using the CUCKOO OPTIMIZATION ALGORITHM. Results show that the best value of the utility function increases as the number of iterations increases. The growth value of the best value of the utility function is higher in the initial iterations and gradually decreases until it reaches zero, indicating that the ALGORITHM has converged to the optimal solution. This reSEARCH fills a gap in the study of investment portfolio OPTIMIZATION using nested OPTIMIZATION models. Additionally, the study finds that shares selected from industries with better performance make up a higher proportion of the portfolio.

In this study, modeling of hydrogen, nitrogen, carbon monoxide, methane and carbon dioxide sorption on UTSA-16 framework extrudates in the pressure swing adsorption process was carried out. The pure gas adsorption of these gases at the pressure range (0 to 80) bars at (298, 313, and 338) K have also been measured in a fixed bed. Langmuir, Toth, Sips, UNILAN, Virial and Dubinin-Astakhov adsorption models were tested on adsorption data to estimate the sorption capacity, intensity and energy. CUCKOO SEARCH OPTIMIZATION ALGORITHM was used for solving the problem to estimate the parameters of adsorption models. The results show that Langmuir isotherm provided the best fit to the equilibrium data with a square of residuals (Fobj) of 0. 022, 0. 2670, 0. 4840, 0. 1813 and 1. 9619 for hydrogen, nitrogen, carbon monoxide, methane and carbon dioxide respectively. There was a good agreement between the predicted theoretical breakthrough curves and the experimental results with Langmuir, Dubinin-Astakhov, Sips, Toth and UNILAN, respectively.