Cultural Algorithm Research Articles

A novel grey seasonal model with time power for energy prediction

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.

Hybrid of non-dominated sorting genetic algorithm II and invasive weed optimization for fan duct surface heat exchanger configuration optimization design

In this research, configuration optimization was conducted for FDSHX (fan duct surface heat exchanger) that is a new type of heat exchanger adopted in aero-engine heat management in recent years. Firstly, a method of Taguchi-ANFIS (Taguchi-Adaptive Neuro-Fuzzy Inference System), which can reduce training data volume utilizing orthogonal experimental matrix, was proposed to construct a rapid response mathematical model between three configuration parameters, including fin pitch, fin thickness, and oil passages number, and two design indicators, including heat transfer capacity and operating weight, based on the data prepared by an experimental validated FDSHX heat transfer capacity calculation method using heat transfer unit simulation. Secondly, a hybrid method of NSGA II-IWO (Non-dominated Sorting Genetic Algorithm II-Invasive Weed Optimization), which can simultaneously obtain the strong abilities of exploration and escaping from trap into local optima, was proposed to drive the constructed response mathematical model to enhance heat transfer through adjusting the three configuration parameters. Optimization comparison was performed between NSGA II-IWO and four classic optimization algorithms including GA (Genetic Algorithm), IWO (Invasive Weed Optimization), CA (Cultural Algorithm), and HS (Harmony Search). This research provides an integrated solution to help mechanical engineers improve the applicability and reasonableness of FDSHX design.

Training Artificial Neural Network with a Cultural Algorithm

Artificial neural networks are amongst the artificial intelligence techniques with their ability to provide machines with some functionalities such as decision making, comparison, and forecasting. They are known for having the capability of forecasting issues in real-world problems. Their acquired knowledge is stored in the interconnection strengths or weights of neurons through an optimization system known as learning. Several limitations have been identified with commonly used gradient-based optimization algorithms, including the risk of premature convergence, the sensitivity of initial parameters and positions, and the potential for getting trapped in local optima. Various meta-heuristics are proposed in the literature as alternative training algorithms to mitigate these limitations. Therefore, the primary aim of this study is to combine a feed-forward artificial neural network (ANN) with a cultural algorithm (CA) as a meta-heuristic, aiming to establish an efficient and dependable training system in comparison to existing methods. The proposed artificial neural network system (ANN-CA) evaluated its performance on classification tasks over nine benchmark datasets: Iris, Pima Indians Diabetes, Thyroid Disease, Breast Cancer Wisconsin, Credit Approval, Glass Identification, SPECT Heart, Wine and Balloon. The overall experimental results indicate that the proposed method outperforms other methods included in the comparative analysis by approximately 12% in terms of classification error and approximately 7% in terms of accuracy.

Hybrid firefly algorithm–neural network for battery remaining useful life estimation

Abstract Accurately estimating the remaining useful life (RUL) of batteries is crucial for optimizing maintenance, preventing failures, and enhancing reliability, thereby saving costs and resources. This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network (FA–NN) model, in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN. The performance of the FA–NN is comprehensively compared against two hybrid models, namely the harmony search algorithm (HSA)–NN and cultural algorithm (CA)–NN, as well as a single model, namely the autoregressive integrated moving average (ARIMA). The comparative analysis is based mean absolute error (MAE) and root mean squared error (RMSE). Findings reveal that the FA–NN outperforms the HSA–NN, CA–NN, and ARIMA in both employed metrics, demonstrating superior predictive capabilities for estimating the RUL of a battery. Specifically, the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154, the CA–NN with a MAE of 9.1189 and RMSE of 22.4646, and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098. Additionally, the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125, the CA–NN at 827.0163, and the ARIMA at 1.16e + 03, further emphasizing its robust performance in minimizing prediction inaccuracies. This study offers important insights into battery health management, showing that the proposed method is a promising solution for precise RUL predictions.

The Art of Online Bodies: Surveillance, Identity, and Collective Narratives

This paper delves into the production of body images in internet art and its interplay with consumer society. Through an analysis of various works related to 'online bodies', the study uncovers the mechanisms of self-exposure and surveillance embedded within these images. These artworks illustrate not only how individuals portray themselves online but also how collective narratives emerge. Furthermore, the paper scrutinizes the role of body images within technological and cultural algorithms, shedding light on their significance in contemporary society.

Cultural Algorithm Guided Policy Gradient with Parameter Exploration

This study explores the integration of cultural algorithms (CA) with the Policy Gradients with Parameter-Based Exploration (PGPE) algorithm for the task of MNIST hand-written digit classification within the EvoJAX framework. The PGPE algorithm is enhanced by incorporating a belief space, consisting on Domain, Situational, and History knowledge sources (KS), to guide the search process and improve convergence speed. The PGPE algorithm, implemented within the EvoJAX framework, can efficiently find an optimal parameter-space policy for the MNIST task. However, increasing the complexity of the task and policy space, such as the CheXpert dataset and DenseNet, requires a more sophisticated approach to efficiently navigate the search space. We introduce CA-PGPE, a novel approach that integrates CA with PGPE to guide the search process and improve convergence speed. Future work will focus on incorporating exploratory knowledge sources and evaluate the enhanced CA-PGPE algorithm on more complex datasets and model architectures, such as CIFAR-10 and CheXpert with DenseNet.

Performance of multiple tuned inerter dampers optimized by a cultural algorithm to control response in buildings subjected to ground acceleration

The reduction of the response parameters of civil structures subjected to the action of wind or ground accelerations is one of the main objectives of the implementation of a structural control system. Many investigations have been developed to make progress and contributions to the design and implementation of effective, reliable, and economical control devices. Therefore, this paper explores the performance of different configurations of multiple Tuned Inerter Dampers (TIDs) in buildings excited by earthquakes. TIDs are passive control devices that enhance a viscous damper by incorporating a novel mechanism known as an inerter. The inerter amplifies an apparent mass that is used to dissipate energy in the controlled system. Four different control alternatives with one, two, three, and four TIDs in the structure are studied. The determination of the optimal design parameters of the different control alternatives was carried out using a novel and very promising optimization process based on a Cultural Algorithm (CA), which has great potential due to being a fast, precise, and reliable method, which is not traditionally associated with the optimization of seismic protection devices. The CA was tuned through a parametric study and compared in terms of performance with an optimization process based on an exhaustive search. A case study of a 12-story building subjected to the action of different ground motions was developed in which the proposed optimal control alternatives were tested. The results showed a significant effectiveness of the control alternatives using multiple TIDs, achieving reductions in peak displacements, RMS values of the maximum displacements and the maximum interstory drifts of up to 54 %.

Оптимизация отраслевых задач в управлении большими данными с использованием эволюционных алгоритмов: сравнительное исследование

This paper proposes a novel approach to solve complex industrial big data management problems using genetic algorithms (GA), particle swarm optimization (PSO), ant algorithms (ACO) and cultural algorithms (CA). The research aims at efficient resource allocation, balancing conflicting objectives such as cost minimization, resource utilization and quality improvement. The proposed approach offers a comprehensive framework that combines the advantages of different optimization techniques, providing decision makers with important insights into optimal big data strategies in their industries. The results of the study show the effectiveness of the hybrid approach in achieving optimal decisions, which improves operational efficiency and strategic decision making in the era of big data.

Strengthening energy transition through effective urban electric demand prediction: A multidisciplinary exploration of AI, policy, and administrative strategies

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.

An Alternative Approach Using the Firefly Algorithm and a Hybrid Method Based on the Artificial Bee Colony and Cultural Algorithm for Reservoir Operation

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.

Blockchain-Based energy trading in Renewable-Based community based Self-Sufficient Utility: Analysis of Technical, Economic, and regulatory aspects

In this ongoing research, a variety of sustainable energy sources, decentralized power generation entities, energy storage solutions, and hybrid electric vehicles with plug-in capability are taken into account. The aim is to present a dependable framework for managing large-scale energy, applicable to both isolated and network-connected modes of renewable hybrid microgrids (HMGs). This framework employs a novel adaptation of Cultural Algorithms (CA) with a two-step modification to minimize the operational expenses of the network. Additionally, it incorporates an innovative intrusion detection system (IDS) based on a deep learning model. The IDS is designed to identify cyber threats related to identity, such as Sybil attacks and masquerading attacks, within the cyber layer. The proposed IDS utilizes the real-time data gathered from the digital twin of the HMG and then uses the LSTM model to differentiate between various signal sources and identify instances of cyberattacks. The data transaction security over the messaging is first preserved by the blockchain technology and later is assessed by the proposed IDS system. To assess the reliability and effectiveness of the proposed framework, a real-time hybrid microgrid is simulated partly in physical layer and partly by digital twin of renewable units to check the model performance.

On-line combustion optimization framework for coal-fired boiler combining improved cultural algorithm, deep learning, multi-objective evolutionary algorithm with improved case-based reasoning technology

It is important to gain the on-line combustion adjustment strategy under ensuring the running working condition of boiler unchanged. Because it can not only significantly improve the whole combustion environment of boiler, but can ensure the stability and safety of boiler in optimization. This article proposes a kind of new-style on-line combustion optimization framework for boiler combustion system, and this study is to quickly acquire the combustion adjustment strategies to improve the whole combustion environment of boiler under ensuring the operating working condition of boiler unchanged. First, improved culture algorithm based on improved particle swarm optimization and double mutation evolution strategy (CIPSODM) and long-short term memory neural network (LSTM) are combined to establish the boiler dynamic combustion model with self-adaptive capability. The improvement of CIPSODM involves two aspects: introducing clustering analysis to the population space of culture algorithm and adding the double mutation thought to the belief space of culture algorithm. And then domain maximum substitution strategy (DMS) is introduced into the decomposition-based multiple-objective evolutionary algorithm (DMS- D/MOEA) to obtain a great deal of combustion optimization cases offline, and an optimization cases base is established by gathering all optimization cases. Whereafter, mutation strategy is added to the case-based reasoning (MSCBR) to online obtain the combustion optimization adjustment strategy with the same running working condition as the current running working condition of the boiler for stable and safety combustion. Meanwhile, to illustrate the availability of the combination of CIPSODM-LSTM, DMS-D/ MOEA and MSCBR, several different on-line optimization approaches are adopted to acquire the combustion adjustment strategies online for comparison. The experiment results illustrated that the thermal efficiency could be increased by 0.588 % and NOx emission could be reduced 14.183 mg/m3 using proposed online combustion optimization approaches. Furthermore, the difference in operating load between adjustment strategy and current boiler reached the minimum value 0.696 MW in all optimization results. Consequently, proposed the on-line combustion optimization framework can gain the on-line combustion adjustment strategies to maximize thermal efficiency and decrease NOx emission so as to improve the whole running environment of boiler.

An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning

This paper presents a hybrid evolutionary algorithm, cultural-particle swarm optimization (C-PSO), which is inspired by the cultural algorithm and the particle swarm optimization algorithm. It is aimed to balance the performance of exploration and exploitation and avoid trapping in the local optima. It introduces a probabilistic approach to update the inertia weight based on the improved metropolis rule. Generating the optimal path without collisions is challenging to ensure vehicles operate safely in real-time implementation. The contributions of C-PSO are to solve the path planning problem of multiple vehicles in modern industrial warehouses, achieving task allocation, fault tolerance and collision avoidance by a dual-layer framework. It was compared with the other algorithms, including PSO, PSO-GA, CA, HS, ABC, HPSGWO, TS and MA, by CEC benchmark functions and statistical tests to demonstrate its great performance with fewer iterations and runtime and the best solutions. It is validated through computational experiments, which involve 15 AGVs and 20 tasks for demonstration.

Hardware-in-the-loop testing of grid-tied PMSG-based wind power generation system with optimum PI parameters

The regulators based on PI control law continue to be the key elements in many of the industrial systems for their control. Likewise, the wind power generation systems (WPGSs) also make extensive use of PI regulators in their control schemes. The enhanced performance of these WPGS depends on proper selection of the PI regulator parameters. This paper deals with the control of a grid-tied permanent magnet synchronous generator (PMSG)-based WPGS wherein, a new attempt has been depicted to apply the most optimum design of the involved PI regulator parameters for the proposed WPGS based on standard performance indices making use of four popular optimization algorithms namely genetic algorithm (GA), cultural algorithm (CA), particle swarm optimization (PSO) and artificial bee colony (ABC). An informative discussion has also been presented which would be useful for practicing engineers/researchers to select flexibly and reasonably the PI regulators parameters meant for the control of the proposed WPGS. A detailed simulation model developed in MATLAB/Simulink has been used to analyze the performance of the proposed PMSG-based WPGS employed with the most optimum values of PI regulator parameters. The performances of WPGS have been compared while the most optimum PI regulator parameters have been included in the control system, and also when incorporating the PI regulator parameters in WPGS control designed via classical d-partition technique. The results obtained under gradually changing wind speed profile show the improvement in the performance of WPGS in terms of peak overshoot, time response and waveform oscillations. The experimental validation of the control performances have been carried out by way of real-time hardware-in-the-loop (HIL) testing making use of Typhoon HIL402 emulator and TMS320F28335 digital signal controller. The obtained real time HIL results are in close agreement to the results obtained in simulations using MATLAB/Simulink. A deviation of less than 4% was observed in the percentage overshoot/undershoot of the PMSG speed and DC link voltage determined through simulation and experimental means under various wind speed conditions.

Landslide susceptibility mapping and management in Western Serbia: an analysis of ANFIS- and SVM-based hybrid models

Landslide susceptibility mapping (LSM) is essential for land-use planning, as it helps to identify areas at risk of landslides and enables effective prevention measures to be taken. Various statistical and machine learning (ML) models are used in LSM, including SVM and ANFIS, which have shown promising results. However, determining which model performs better remains a key challenge. To address this issue, this paper aims to compare six hybrid models constructed with two well-known and powerful ML models, namely SVM and ANFIS, and three meta-heuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Cultural Algorithm (CA), for LSM in a case study in western Serbia. In the process of building the models, 359 landslide sites and 14 determinants were used. The accuracy of the models was evaluated using several indexes, including Root Mean-Squared Error (RMSE), coefficient of determination (R2), and Area under the Receiver Operating Characteristic Curve (AUROC). The modeling results showed that the SVM-GA model has the highest accuracy (AUROC = 0.78) in predicting landslide incidence, followed by the ANFIS-GA (AUROC = 0.775), SVM-CA (AUROC = 0.773), ANFIS-DE (AUROC = 0.771), SVM-DE (AUROC = 0.76), and ANFIS-CA (AUROC = 0.65) models in validation phase. Therefore, the study suggested that SVM-based hybrid models are more accurate than ANFIS-based models for LSM, and thus, modelers may use SVM-based hybrid models for such applications. This study provides valuable insights into identifying the most appropriate and effective models for LSM, which can help to mitigate the risks associated with landslides and ensure sustainable land-use practices.

FEATURE EXTRACTION USING AT-CONVLSTM BASED CULTURAL ALGORITHM FOR IMAGE UNDERSTANDING

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.

Applying Cognitive Methodology in Designing On-Line Auto-Tuning Robust PID Controller for the Real Heating System

A novel design and implementation of a cognitive methodology for the on-line auto-tuning robust PID controller in a real heating system is presented in this paper. The aim of the proposed work is to construct a cognitive control methodology that gives optimal control signal to the heating system, which achieve the following objectives: fast and precise search efficiency in finding the on- line optimal PID controller parameters in order to find the optimal output temperature response for the heating system. The cognitive methodology (CM) consists of three engines: breeding engine based Routh-Hurwitz criterion stability, search engine based particle swarm optimization (PSO) and aggregation knowledge engine based cultural algorithm (CA). Matlab simulation package is used to carry out the proposed methodology that finds and tunes the optimal values of the robust PID parameters on-line. In real-time, the LabVIEW package is guided to design the on-line robust PID controller for the heating system. Numerical simulations and experimental results are compared with each other and showed the effectiveness of the proposed control methodology in terms of fast and smooth dynamic response for the heating system, especially when the control methodology considers the external disturbance attenuation problem.

New Meta-heuristic - Based Approach for Identification and Control of Stable and Unstable Systems

Nowadays, the use of meta-heuristic algorithms (MAs) for tackling complicated engineering issues has shown significant promise, therefore applying MAs to optimum model parameters and PID parameters can be quite beneficial. As a result, this paper looks at the capabilities of four recently released resilient MAs in optimizing model parameters and PID parameters for various system behaviors. Hence, these four meta-heuristic algorithms are used such as Ant Colony Optimization (ACO), Cultural Algorithm (CA), Invasive Weed Optimization (IWO), and Black Hole Algorithm (BHA). The key contribution of this study is the employment of many meta-heuristics at the same time with the same objective function while taking into consideration each algorithm parameters for identification and control, then compared to traditional techniques such as Least square (LS) and Reference Model (RM). Thus, the most efficient algorithm is the one that yields the lowest cost function, has the lowest standard deviation (SD), and uses the least amount of CPU time. Regarding identification, simulation findings showed that CA algorithm has the best cost, lowest standard deviation (SD) and fewest CPU time 2.7838e-13, 7.1108e-13 and 3.1395(s), respectively. As for control system, it is shown that created intelligent-based controllers are more dependable than reference model controllers in stabilizing the behaviors of the various examined processes, with the IWO algorithm finds the best gains of PID and converges the fastest with best cost 3.2905e-10 and CPU time 48.8732(s). Moreover, ACO and BHA both failed to achieve satisfactory results in terms of accuracy and CPU time compared to others algorithms. Additionally, studies also showed that optimization methods has good performance, resilient and effective.

A cultural evolution with a modified selection function and adaptive [formula omitted]-cognition procedure for numerical optimization

In recent years, several population-based evolutionary and swarm algorithms have been developed and used in the literature. This work introduces an improved Cultural Algorithm with a modified selection function and a dynamic α-cognition procedure to handle a variety of challenging numerical optimization problems. The modified selection function is used to support a balanced evolutionary search. A process that starts with a clearer exploration early in the search process and gradually begins to focus on exploitation towards the end of the search process. This work uses the elites of each knowledge source that are at a certain distance from each other. The dynamic α-cognition procedure assists in providing effective learning of individuals through preserving the diversity of the population during the evolution process. In this procedure, each individual is able to learn from the top α% individuals controlled by its knowledge source in the belief space, where the proportion of the affecting subpopulation (α) is adaptively modified during the evolution. The performance of the proposed work has been evaluated on the CEC’2010 and CEC’2013 benchmark suites developed for the special sessions on large-scale global optimization problems. An appropriate comparative study with the best results in the literature is presented. The results confirm how the merits of the improved Cultural Algorithm can achieve superior performance over other cutting-edge algorithms for these data sets.

Fuzzy adaptive forensic-based investigation algorithm for optimizing frequency-constrained structural dome design

This work develops a novel fuzzy adaptive forensic-based investigation algorithm (FAFBI) for optimizing frequency-constrained structural dome design. The Wilcoxon rank sum test of FAFBI and compared algorithms indicates that FAFBI is significantly better at finding optimal value than artificial bee colony, cultural algorithm, differential evolution, firefly algorithm, forensic-based investigation algorithm, genetic algorithm, harmony search, particle swarm optimization, simulated annealing, symbiotic organisms search, and teaching–learning-based optimization. Next, the proposed FAFBI algorithm is used to optimize the weight of steel dome structures under frequency constraints. Design cases involve 52-bar, 120-bar, 600-bar, 1180-bar, and 1410-bar​ domes. The results of optimizing reveal that the FAFBI can identify lighter domes while meeting the required frequency constraints than can the methods in the literature. Accordingly, the proposed algorithm is an effective tool for solving mathematical optimization problems and for use in the design phase of structural construction. The source code is currently available from https://www.mathworks.com/matlabcentral/fileexchange/126435-fuzzy-adaptive-forensic-based-investigation-algorithm.