Search

In reservoir operation rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This study proposes an approach to optimizing reservoir operation rule curves (RORCs) using intelligent optimization techniques from the firefly algorithm (FA) and a unique combination method utilizing the artificial bee colony and cultural algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORCs for flood control. The proposed model was used to determine the optimal flood control RORC for the Nam-Oon Reservoir (NOR) in northeastern Thailand. A minimum frequency and minimum average of excess water were provided as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORCs involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data synthesized from 1000 events. The results demonstrated that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, excess water scenarios were smaller than those using the current RORC. The results showed that the excess water scenario using the RORC obtained from the proposed model can reduce the excess water better than the current RORC usage scenario. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC. [ABSTRACT FROM AUTHOR]

Among renewable energy sources, investments in solar and hydrogen energy as eco-friendly energy sources are continuously expanding. The energy generated by solar photovoltaic (PV) panels, varies during the day. In order to ensure energy continuity, hybrid systems are being developed by integrating solar energy systems with different energy systems. Solar-assisted hydrogen production energy systems have a number of advantages, such as having zero carbon emissions, being able to be used as fuel in fuel cells, and having a high energy density of hydrogen. The sustainable energy obtained from solar energy is used to meet the energy demand in numerous applications due to the high solar energy potential in Mediterranean Region. In this article, solar PV panel output power prediction based on Artificial Neural Network (ANN) and Cultural Algorithm-Artificial Neural Network (CA-ANN) for a solar-assisted hydrogen production energy system have been performed. In this study, the real time data of a 4 MW solar PV power plant is used to estimate solar PV output power. Predicting the output power of PV power plants with high accuracy is of great importance in the realization of energy management, hydrogen production and storage strategies. The numerical results obtained at the end of the study are presented. The results obtained with the ANN and CA-ANN methods are given in detail. In addition, the hydrogen production amount in the Mediterranean region is calculated using MATLAB/Simulink software. The analysis results show that the Mediterranean region has high efficiency hydrogen production potential. [ABSTRACT FROM AUTHOR]

Distribution networks transmit the electrical power generated by generation systems to consumers. The distribution network poses a significant challenge in a power system, as it incurs the highest losses due to its low voltage and high current characteristics. Therefore, addressing the issue of reducing distribution network losses is crucial. Over the past 46 years, distribution network reconfiguration (DNR) has been extensively studied as a combinatorial optimization problem. DNR is one of the most intensively investigated topics, accompanied by new challenges. This study focuses on the optimal reconfiguration of the distribution network and the allocation of shunt capacitors to reduce losses and enhance voltage profiles. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation by using direct load flow to minimize power loss and voltage deviation. By comparing the performance of these two algorithms and examining their advantages and disadvantages, the appropriate algorithm can be selected for future studies. The performance of the simulations was verified using MATLAB software on IEEE 33-bus and IEEE 69-bus systems. [ABSTRACT FROM AUTHOR]

This research presents a novel approach for feature extraction in image understanding, utilizing an AT-ConvLSTM-based Cultural Algorithm. The Proposed CA-AT-ConvLSTM leverages the power of deep learning through AT-ConvLSTM architecture while optimizing the feature extraction process using Cultural Algorithms. This synergistic approach enhances the efficiency and accuracy of image understanding tasks, making it suitable for a wide range of applications, from computer vision to pattern recognition. The experimental results demonstrate the superiority of the proposed technique over traditional methods, highlighting its potential in advancing the field of image analysis. [ABSTRACT FROM AUTHOR]

In the business sector, predicting the movement of the Stock Exchange of Thailand (SET) index is challenging. Due to worldwide stock market fluctuations, investors commonly invest in price-changing businesses solely in the long term. Therefore, an accurate SET index movement prediction method is significant for investment purposes and has been the goal of many previous studies. Some studies have indicated that neural network (NN) models perform more effectively and accurately than traditional statistical models; accordingly, NNs employing backpropagation (BP) with sigmoid and smooth adaptive activation functions (SAAFs) and 10 metaheuristic algorithms to determine the initial prediction weights were developed in this study. An experiment was conducted using a Thailand SET50 index dataset, and the results revealed that the model utilizing SAAFs with a cultural algorithm (CA) for weight initialization yielded more precise and efficient predictions than those of other competing models. This finding indicated the possibility of applying the proposed method for SET index movement prediction in the future. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for robustly estimating the parameters of the probability integral method (PIM). According to the framework, the initial robust estimates of the PIM parameters are firstly obtained by combining the cultural algorithm and rand particle swarm optimisation (CA-rPSO) with the LTS method. As a byproduct, an initial standard deviation can be calculated and used to determine the initial weights of the measurements according to the Institute of Geodesy and Geophysics (IGGIII) down-weighting scheme. Meanwhile, a modified CA-rPSO (referred to as CA-rPSO-IGGIII) is constructed, where the IGGIII scheme is introduced to alleviate the adverse influence of outliers. Then, the initial robust estimates and the standard deviation can act as a priori information for the CA-rPSO-IGGGIII to search for the optimal estimates. Experiments with simulated and real data demonstrate that the proposed method can robustly estimate the PIM parameters. [ABSTRACT FROM AUTHOR]

In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works. [ABSTRACT FROM AUTHOR]

Evolutionary computing algorithms are computational intelligent systems that are used in a wide range of research applications, primarily for optimization. In this paper, an artificial neural network (ANN), a machine learning technique, is used to classify the data. The weights associated with each neuron and the architecture of the neural network always bias the output of the network model. With prior knowledge or trial and error techniques, different metrics or objectives can be used to optimise these weights. The optimization of weights using multiple objectives refers to a "multi-objective optimization problem." In this paper, an evolutionary cultural algorithm is used to optimise weights in ANN, and the results are reported with improved accuracy. Three benchmark datasets for autism screening data are used, trained, and tested for model accuracy in the classification: toddlers (1054,19), children (292,21), and adults (704,21).With the support of the domain expert, real-time data were collected from parents and caregivers and totalled over 1000 records, with a moderate difference in attributes based on CARS-2 (Childhood Autism Rating Scale, 2nd Edition) for ASD screening. In this paper, the proposed model is compared using a curve-fitting mathematical technique. The proposed model is trained and tested, and the results showed that it outperformed other algorithms in terms of precision, accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

The fluctuation of seasonal time series has attracted considerable attention. However, the complexity of socio-economic development and the variability of influencing factors make accurate predictions of seasonal time series more difficult. A new discrete grey seasonal model, namely DGSTPM(1,1) , is established, which employs the time power term based on the cultural algorithm to effectively deal with the time sequences characterized by nonlinear tendency and periodic fluctuations. Firstly, the operating mechanism of the DGSTPM(1,1) model is expounded by combining the time power item with the traditional grey seasonal model to enhance the forecasting capability. Then, the optimal time power index of the model can be determined by incorporating the cultural algorithm. Subsequently, several properties are thoroughly discussed to illustrate the superiority and uniformity of the new model. Finally, two energy-related cases with different data features and sample sizes are applied to identify the efficacy and robustness of the DGSTPM(1,1) model, referring to monthly crude oil production in the United States and quarterly coke production in China. Based on the various error criteria, SPA and DM tests, the results indicate that the DGSTPM(1,1) outperforms its competitors both in simulation and prediction phases, including DGSTM(1,1), SGM(1,1), SDTGM, FTGM, SARIMA, LSSVM, BPNN, and LSTM models. Meanwhile, the new model can effectively address seasonal time series with nonlinear characteristics owing to its dynamic time power item with the support of the cultural algorithm. [ABSTRACT FROM AUTHOR]

• Developing a PLSTM model for electric load demand forecast. • Developing a hybrid WOA-CA for adjusting Peephole PLSTM. • Making stability analysis on the real-time dataset of an urban area. This research endeavors to strengthen energy transition in urban management through the enhanced accuracy of electric load demand forecasting within a multi-faceted approach considering legal and policy decision making associate with the proposed problem. Firstly, a prediction model is devised utilizing an evolving Peephole LSTM (PLSTM). This sophisticated architecture incorporates peephole connections, allowing for enhanced information flow within the network. Subsequently, a hybrid evolutionary algorithm is introduced for fine-tuning the parameters of the peephole LSTM (PLSTM). This hybrid approach amalgamates the whale optimization algorithm (WOA) and cultural algorithm (CA), harnessing their complementary strengths for optimal parameter configuration. The study extends beyond model development to address the practical implications of real-time electric load forecasting. A comprehensive stability analysis is conducted on datasets sourced from an urban area, offering insights into the model's reliability and performance under dynamic conditions. This tripartite investigation not only contributes to the advancement of predictive modeling in electric load demand forecasting but also underscores the significance of robust parameter optimization and stability assessments for real-world applicability in urban energy management. The simulation results on the practical dataset clearly show the high robustness and capabilities of the proposed model. By combining cutting-edge model architecture, hybrid evolutionary optimization, and thorough stability assessments, this study not only contributes to the forefront of predictive modeling in electric load forecasting but also emphasizes the critical importance of parameter optimization and stability evaluations for the seamless integration of such models into the dynamic landscape of urban energy management. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have been widely used to solve complex engineering optimization problems with large search spaces and nonlinearity. Both cultural algorithm (CA) and genetic algorithms (GAs) have a broad prospect in the optimization field. The traditional CA has poor precision in solving complex engineering optimization problems and easily falls into local optima. An efficient hybrid evolutionary optimization method coupling CA with GAs (HCGA) is proposed in this paper. HCGA reconstructs the cultural framework, which uses three kinds of knowledge to build the belief space, and the GAs are used as an evolutionary model for the population space. In addition, a knowledge-guided t-mutation operator is developed to dynamically adjust the mutation step and introduced into the influence function. HCGA achieves a balance between exploitation and exploration through the above strategies, and thus effectively avoids falling into local optima and improves the optimization efficiency. Numerical experiments and comparisons with several benchmark functions show that the proposed HCGA significantly outperforms the other compared algorithms in terms of comprehensive performance, especially for high-dimensional problems. HCGA is further applied to aerodynamic optimization design, with the wing cruise factor being improved by 23.21%, demonstrating that HCGA is an efficient optimization algorithm with potential applications in aerodynamic optimization design. [ABSTRACT FROM AUTHOR]

Based on the dual-inheritance framework of cultural evolution, an improved multiobjective cultural algorithm (IMOCA) with a multistrategy knowledge base is presented in this paper. Inspired by the original versions of the cultural algorithm (CA), four basic types of knowledge sources, i.e., normative, situational, topographical and historical knowledge, are effectively utilized in the proposed IMOCA. Several modifications with the knowledge base of the IMOCA are made to tackle the characteristics of the multiobjective problem. Situational knowledge is used as an external repository for storing elite individuals, and the redesigned topographical knowledge functions as a search engine to broaden the expansion of the obtained solution set. The historical knowledge used in the IMOCA aims to select a productive knowledge source to generate new individuals. Furthermore, a simple mutation scheme is introduced into the knowledge base as an influence function for the purpose of fine tuning in the late stage of search. After configuring the parameters used in IMOCA, two classic benchmark suites, i.e., WFG and MaF, are used to assess the performance of the IMOCA in approaching the Pareto fronts (PFs) with accuracy and diversity. Nondominated solution sets obtained by the IMOCA are compared with 8 state-of-the-art multiobjective algorithms available in the literature. A statistical analysis is conducted, which reveals that, by modifying the basic knowledge structure of the CA, the proposed multiobjective cultural algorithm is competent enough to handle multiobjective problems with competitive performance. [ABSTRACT FROM AUTHOR]

A novel culture-based multiswarm artificial bee colony (CMABC) algorithm was proposed to address dynamic optimization problems. The historical experience of sub-swarms is preserved as cultural knowledge to guide the subsequent evolutionary process. Experiments were conducted on the moving peaks benchmark function. The results show that the CMABC algorithm was better than, or at least comparable to, the basic ABC algorithm, and other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness; state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear; and topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory.

This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved.

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effects of climate change have been already experienced globally. The uncertainties of climate and land cover changes in the watershed above the reservoir have direct impacts on the inflow into the reservoir and the water resource management that uses reservoir rule curves. This study aims to assess the impacts of climate and land cover changes on the inflow into the Huai Luang Reservoir, Udonthani province during the period of 2018-2067 by using the simulation of two climate scenarios with the data from the PRECIS model under the emission conditions of B2. The prediction of future inflow has used the InVEST model. The improvement of the reservoir rule curves has used the Cultural Algorithm (CA) technique connected with the reservoir system simulation model. Monthly inflow data flowing into the reservoir from the past 33 years has been used to synthesize 1,000 events in order to evaluate the efficiency of the new obtained reservoir rule curves. Therefore, the data results of water shortage and excess water discharge situation have been illustrated in form of frequency, magnitude, duration of events, average amount of water, and maximum amount of water. The results have showed that average rainfall and temperature have tended to increase. The optimal reservoir rule curves improved by the CA technique can effectively reduce the frequency of water shortage situations and the one of the excess water discharge occurring during inflow changes in normal situation, and future situation under emission scenario B2. [ABSTRACT FROM AUTHOR]

In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.

Evolutionary algorithms have been widely used to solve complex engineering optimization problems with large search spaces and nonlinearity. Both cultural algorithm (CA) and genetic algorithms (GAs) have a broad prospect in the optimization field. The traditional CA has poor precision in solving complex engineering optimization problems and easily falls into local optima. An efficient hybrid evolutionary optimization method coupling CA with GAs (HCGA) is proposed in this paper. HCGA reconstructs the cultural framework, which uses three kinds of knowledge to build the belief space, and the GAs are used as an evolutionary model for the population space. In addition, a knowledge-guided t-mutation operator is developed to dynamically adjust the mutation step and introduced into the influence function. HCGA achieves a balance between exploitation and exploration through the above strategies, and thus effectively avoids falling into local optima and improves the optimization efficiency. Numerical experiments and comparisons with several benchmark functions show that the proposed HCGA significantly outperforms the other compared algorithms in terms of comprehensive performance, especially for high-dimensional problems. HCGA is further applied to aerodynamic optimization design, with the wing cruise factor being improved by 23.21%, demonstrating that HCGA is an efficient optimization algorithm with potential applications in aerodynamic optimization design.

In order to fully use the advantages of the PVS algorithm and CA, a hybrid co-evolutionary cultural algorithm based on PVS is proposed. In this algorithm, a co-evolutionary mechanism between two cultural algorithms sub-populations is established to take full advantage of CA. The objective, like other meta-heuristics, is to find global optimum or quasi-optimal solutions for a given function. To verify the performance of the proposed algorithm, it will be tested on a set of benchmark functions. The results demonstrated the superior effectiveness of CA-PVS compared with other meta-heuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

Economic-emission load dispatch uses the fuel cost variables and gas emission in a minimized way to obtain an optimal operation in generation units in a power plant, guaranteeing the supply of demand. The first variable is definitive to ensure business continuity and the second to comply with environmental legislation and no degradation of the environment. This paper analyzes the use of a new computational optimization algorithm based on the cultural algorithm (CA), improved with local search techniques simulated annealing and Tabu search, using data from a real power plant with 10 generators and the system of the IEEE with 13 generating units. The application has two options of operation: the classic one, which operates with all generators seeking to minimize the cost and emission meeting the specified demand; and the controlled one, which turns off the generators that have the highest incremental fuel cost but guaranteeing the demand and reducing the emission of gases. Simulations were performed on the six possible options in this application. The results obtained were compared with each other and with the results of other techniques reported in the literature. The local search that improved the CA and the new way of updating topographic knowledge allowed the results to be better than those found by other metaheuristics that solved the same problem of the real plant and the IEEE system.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is a common and most preferable research method for bringing out the fascinating relations between the variables provided in any data set. It brings out the knowledge by satisfying the user defined values and criteria measures specified by the researcher. Frequent item set generation is a well-known method carried out by many researchers to retrieve interesting correlations among the variables that helps in decision making. The accuracy of the brought out rules by ARM is good enough to provide a conclusion on research studies. This can be improved by incorporating optimization like heuristic search techniques. In this paper cultural algorithm is used to improve the performance of rule mining by optimization which is required for categorizing the risk level of ASD individuals. Optimization is utilized in health care domain for generating optimized rules to analyze the frequently combined attributes among the patient's data. It gracefully improves the result finding process which will be tranquil to conclude the decision. The Cultural algorithm fit in to the larger course of evolutionary algorithms that is inspired by natural evolution. In this paper multi objective optimization technique is proposed by incorporating ARM and cultural algorithm by considering different objectives namely support, confidence, lift and completeness of the rule to find the positive association of Autism Spectrum Disorder (ASD) screening data features with positive class. The result of this research depicts the positive association with improved performance along with reduced number of rules. [ABSTRACT FROM AUTHOR]

In this paper, five successful nature inspired algorithms; the artificial tree algorithm (AT), the particle swarm optimization (PSO), the genetic algorithm (GA), the cultural algorithm (CA), and the cuckoo search algorithm (CS) have been compared on multilevel image thresholding. The segmentation process is based on the Levine and Nazif intra class uniformity criterion which is seen as an optimization problem. The comparison performances are in terms of the value of the objectif function, the peak signal to noise ratio (PSNR) and the computation time. Empirical results over different benchmark images for different threshold numbers reveal the robustness, the reliability and the rapidity of the cultural algorithm (CA). [ABSTRACT FROM AUTHOR]

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased. [ABSTRACT FROM AUTHOR]

This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved. [ABSTRACT FROM AUTHOR]

In this article, a new hybrid algorithm is proposed which was based on the elephant herding optimization (EHO) and cultural algorithm (CA), known as elephant herding optimization cultural (EHOC) algorithm. In this process, the belief space defined by the cultural algorithm was used to improve the standard EHO. EHO is motivated by herding behavior of the elephant groups. These behaviors are modeled into two operators including clan updating operator and separating operator. In EHOC, based on belief space, the separating operator is defined, which is able to create new local optimums in search space, to improve the algorithm search ability and to create an algorithm with an optimal exploration–exploitation balance. The CA, EHO, and EHOC algorithms are applied to eight mathematical optimization problems and four truss weight minimization problems, and to assess the performance of the proposed algorithm, the results are compared. The results clearly indicate that EHOC is capable of accelerating the convergence rate effectively and can develop better solutions compared to the CA and EHO. In addition, it can produce competitive results in comparison with other metaheuristic algorithms in the literature. [ABSTRACT FROM AUTHOR]