Imperialist Competitive Algorithm Research Articles

Estimation of Solar Power Generation Through the Use of Effective Forecasting Algorithms

The unpredictable nature of solar power generation poses significant challenges to energy management, particularly in power grids with high solar penetration, leading to potential imbalances between supply and demand. This has increased interest in developing accurate forecasting methods for photovoltaic power generation. This study focuses on short-term forecasting of solar power generation using MATLAB, based on historical generation trends through three different hybrid models. It uses a multilayer perceptron neural network as the forecasting tool, which is optimized through various algorithms to fine-tune the network's hyperparameters, such as the weights and biases of the input and hidden layers, for optimal performance. The Root Mean Square Error is used as the objective metric to minimize the discrepancy between actual and predicted values. The models use Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm, and Genetic Algorithm for network optimization. This research analyzes PV generation data from Belgium in July and August 2022, taken at 15-minute intervals, and evaluates model performance using six different error metrics, including Mean Absolute Error. The results show the accuracy and reliability in forecasting solar power generation time series.

Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models.

This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.

Imperialist competitive algorithm for unrelated parallel machine scheduling with sequence-and-machine-dependent setups and compatibility and workload constraints

In this paper, we present an in-depth analysis of the unrelated parallel machine scheduling problem (UPMSP) in the context of washing machine production at Vestel Electronics. Vestel Electronics is a leading producer of washing machines and holds a noteworthy market position in the European consumer electronics industry. The production process at Vestel Electronics is make-to-order (MTO) and requires 20 assembly lines to produce 200 different products. The decision maker must consider several important factors while forming the production schedule, including job-assembly line compatibility, the release times and due dates of the jobs, and a workload balance among the different assembly lines. This study aims to develop an algorithm to minimize the total earliness and tardiness while considering sequence-and-machine-dependent setups, unequal release times, machine-job compatibility restrictions, and workload balance requirements. To address this complex scheduling problem, we propose a novel algorithm, the imperialist competitive algorithm (ICA), which has a strong exploration-exploitation balance while mimicking real-world dynamics. Our numerical results show that the ICA significantly outperforms the current practice in Vestel by 39% and the best-performing algorithms in the literature by 23% and 12%.

A New Mathematical Model for Cell Layout Problem considering Rotation of Unequal Dimensions of Cells and Machines

One of the main concepts in group technology (GT) is the cellular manufacturing system (CMS) with three main problems of cell formation (CF), cell layout (CL), and cell scheduling (CS). This paper studies the cell layout problem (CLP), aiming to find the optimal layout of machines within each cell (intracellular layout) and the optimal layout of cells in each workshop (intercellular layout). To adapt to reality, the dimensions of the cells and machines (inside each cell) were considered unequal, and also the cells and machines could rotate. We believe that a cellular layout that assumes unequal dimensions of the cells and machines can be used for batch production. This kind of production has a wide variety of low to medium demand. Furthermore, a cellular layout can be applied in CMSs and also in noncontinuous industries that have a job shop layout. Our main contribution is considering the possibility of rotating the cells and machines inside the cells. For this purpose, a mixed nonlinear programming model was developed to solve the CLP with the minimum cost of intracellular and intercellular material flows. The proposed nonlinear model was first converted into a linear model, and then a problem was generated and solved with GAMS software to validate the resulting linear model. This model finds the best layout of cells within the workshop and the best layout of machines inside each cell. Then, because of the NP-hardness of the CLP and the fact that even exact methods cannot solve large-scale examples in an acceptable computational time, an imperialist competitive algorithm (ICA) was designed and used to solve the problem. To evaluate the efficiency of the proposed algorithm, its numerical results in small dimensions were compared with the results of GAMS software. In large dimensions, 30 random problems were created, and the results of ICA were compared with the results of the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Finally, the parameters of the three meta-heuristic algorithms were set by the Taguchi method. Numerical results indicated that ICA was superior to both the PSO algorithm and GA. It could also achieve efficient solutions in a shorter computational time.

Modified imperialist competitive algorithm for aircraft landing scheduling problem

Modified imperialist competitive algorithm for aircraft landing scheduling problem

Navigating Legal Pathways for Accelerating Urban Energy Transition: A Comprehensive Deep Analysis of Photovoltaic Power Prediction and Policy Instruments

This research proposes a navigation method that explores how to accelerate urban energy transformation through legal means from the perspective of energy law, in order to improve the accuracy of photovoltaic output power prediction in sustainable energy transformation. Firstly, a deep learning model based on adversarial autoencoder (AAE) architecture was introduced to predict photovoltaic output power according to clean environment policies. Secondly, in order to effectively fine tune the parameters of AAE, a heuristic optimization method based on the Imperial Competition Algorithm was proposed to optimize the AAE configuration to adapt to the unique characteristics of photovoltaic systems, thereby improving prediction accuracy. In addition, the study analyzed the challenges and opportunities faced by current urban energy transformation, and then proposed sustainable ways to accelerate urban energy transformation from the aspects of formulating transformation plans, promoting technological innovation, promoting green transportation, promoting building energy conservation, and developing circular economy. The synergistic effect of deep learning and heuristic optimization, combined with strict stability and sensitivity assessment, puts this research at the forefront of advancing photovoltaic output power prediction methods, which is of great significance for optimizing renewable energy utilization and grid integration.

Optimizing weighted voltage mode control for enhanced output cross-regulation in multi-output DC/DC converters

Weighted voltage mode control is a widely used method for regulating multiple output DC-DC converters, but inconsistent outcomes are often observed in design due to the complexity of the weighting variable optimization. This study proposes an optimization-based approach to accurately estimate the optimal weighting factors for improved output voltage regulation in multiple output forward DC-DC converters. Three optimization algorithms, the Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are compared for their speed and accuracy in estimating the weighting factors. Additionally, a Fuzzy Logic Controller (FLC) is used to further reduce the overall steady-state error and improve transient characteristics. Simulations are performed using the MATLAB/Simulink software, and the results show that the proposed strategy significantly enhances output cross-regulation in multiple output forward DC-DC converters. The ICA-based weighting factor estimator is found to be the most effective algorithm among the three optimization algorithms tested. The main contribution of this study is to provide a more efficient and accurate method for estimating the weighting factors in multiple output forward DC-DC converters, which can lead to improved performance and reliability in various applications.

Design optimization of additively manufactured sandwich beams through experimentation, machine learning, and imperialist competitive algorithm

The primary aim of this study was to optimize 3D-printed sandwich beams incorporating auxetic cores. Four key design variables were considered: core thickness, width, and two specific geometric parameters related to the auxetic core–the cell's wall thickness-to-length ratio and the cell angle. Initially, sixteen sandwich samples, each varying in geometric parameters, were printed following Taguchi's experimental design. These samples underwent a three-point bending test, and the resulting data were used to train a deep neural network (DNN). The trained DNN proficiently predicted the bending response of the structures. Another network, using a similar architecture, was trained to precisely predict the structure's mass. These trained networks were then integrated into the imperialist competitive algorithm (ICA) to optimize the sandwich beams design for minimal mass. A contour plot was subsequently generated to visually illustrate the minimum required mass distribution for sandwich beams under various conditions and constraints, considering applied bending forces and maximum allowable deflection. The optimization results underscored the importance of fabricating and testing an adequate number of sandwich samples, aligning with the number of design parameters. The results provide a valuable reference for future research on designing advanced 3D-printed lightweight structures, including integrating machine learning techniques and metaheuristic algorithms.

Improving churn prediction using imperialist competitive algorithm for feature selection in telecom

Improving churn prediction using imperialist competitive algorithm for feature selection in telecom

Collaborative Beamforming Optimization using Imperialist Competitive Algorithm

Collaborative Beamforming Optimization using Imperialist Competitive Algorithm

River water quality management using an integrated multi-objective optimization-simulation approach based on bankruptcy rules.

The aim of this research is to allocate the river's self-purification (acceptance capacity of pollution) fairly between the beneficiaries (pollutant sources) using bankruptcy theory. For this purpose, four bankruptcy rules (CAE, CEL, P, and TAL) were called using the link of the water quality simulation model (QULA2Kw) to an evolutionary optimization algorithm (multi-objective imperialist competition algorithm (MOICA)). The objective functions were reducing polluters' wastewater treatment costs and preventing biochemical oxygen demand (BOD) violations of the standard level along the river. The applicability of the approach is demonstrated by the case study that was carried out on the Dez River in Iran. According to the results, the CEL scenario is the most effective method for the Dez River when taking into account the most optimal state for both objective functions (selecting the best compromise solution from the Pareto front). This is because it has the lowest violation value of the standard level for BOD along the river when compared to other scenarios. Alternatively, when considering Solution 20, which focuses on the maximum cost of treating the polluters while staying within the acceptable level of pollution in the river, the results indicated that the CEA rule emerged as the most favorable option. This is due to its lower treatment cost (156.9 (1000$)) and higher pollution discharge to the river (681.91 g/s).

Compressive strength evaluation of ultra-great workability concrete by creating hybridized regression analysis

In the present article, the applicability of proposing hybridized regression evaluation on the ultra-great workability concrete ( UGWC ) was assessed with the goal of declining the assigned time and experimental works. UGWC can be included in several by-products or physical elements. For proposing suggested models, several hybridized regressions were produced, in which the best values of principal attributes of support vector regression (SVR) were clarified by metaheuristic methods such as sine cosine algorithm (SCA), Imperialist competitive algorithm (ICA), called ICA-SVR, and SCA-SVR. The criteria supply that both hybridized ICA-SVR and SCA-SVR models conclude justifiable ability in estimation outline. Taking into account integrated models, the ICA-SVR results in the weakest accuracy compared to SCA-SVR, with R 2 and VAF accounting for 0.9098, and 90.66%, respectively. The highest and most incredible performance owned by the SVR model hybridized with the SCA algorithm, where it was able to receive the largest value of R 2 and VAF in both training and testing datasets, and the smallest value of RMSE , and MAE in both training and testing datasets. MAE value in the training and testing phase for the ICA-SVR model is roughly fourfold and six times than SCA-ANFIS, which depicts the wonderful performance of SCA-SVR in the estimation system. So, the hybridized SCA-SVR can receive the highest precision in comparison with ICA-SVR as well as the published essay ( R 2 =0.801).

Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model

PurposeThe proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).Design/methodology/approachIn addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.FindingsResults have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.Research limitations/implicationsIn this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.Originality/valueIn this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading

Determining the bearing capacity of a strip footing under inclined loading is crucial in designing foundations. Due to the complex correlations, the subject area remains predominantly unexplored, or it has been simulated using only limited datasets. This paper presents the development of a prediction model based on machine learning (ML), leveraging advanced hybrid artificial neural network (ANN) models for estimating the bearing capacity of strip footings under inclined loading. The ANN models are hybridized with four different optimization algorithms, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE), which enhance the accuracy and efficiency of the predictive capabilities of ANN. The models are trained on a dataset of 920 records, and their performance is evaluated using a range of significant performance metrics. The ANN-ICA model achieved the highest rank in the score analysis (R2 =0.912, RMSE=0.165 in testing), followed by ANN-ALO and ANN-ACO. To reinforce the trustworthiness of the predictions, external validation is employed, and visual analysis is conducted using the Taylor diagram. The findings suggest that the proposed models are robust, and the incorporation of optimization techniques has improved the performance of traditional ANNs. The research findings have significant implications for the field of geotechnical engineering, providing engineers and researchers with valuable insights into the applicability of hybrid artificial neural network (ANN) models and alternative machine learning (ML)-based prediction models in assessing the bearing capacity of strip footings.

Performance Analysis of Meta-Heuristic Algorithms for Optimal PI Tuning of PFC

The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo Optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed Optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies.

Solving the Combined Heat and Power Economic Dispatch Problem in Different Scale Systems Using the Imperialist Competitive Harris Hawks Optimization Algorithm.

The aim of electrical load dispatch (ELD) is to achieve the optimal planning of different power plants to supply the required power at the minimum operation cost. Using the combined heat and power (CHP) units in modern power systems, increases energy efficiency and, produce less environmental pollution than conventional units, by producing electricity and heat, simultaneously. Consequently, the ELD problem in the presence of CHP units becomes a very non-linear and non-convex complex problem called the combined heat and power economic dispatch (CHPED), which supplies both electric and thermal loads at the minimum operational cost. In this work, at first, a brief review of optimization algorithms, in different categories of classical, or conventional, stochastic search-based, and hybrid optimization techniques for solving the CHPED problem is presented. Then the CHPED problem in large-scale power systems is investigated by applying the imperialist competitive Harris hawks optimization (ICHHO), as the combination of imperialist competitive algorithm (ICA), and Harris hawks optimizer (HHO), for the first time, to overcome the shortcomings of using the ICA and HHO in the exploitation, and exploration phases, respectively, to solve this complex optimization problem. The effectiveness of the combined algorithm on four standard case studies, including 24 units as a medium-scale, 48, 84, units as the large-scale, and 96-unit as a very large-scale heat and power system, is detailed. The obtained results are compared to those of different algorithms to demonstrate the performance of the ICHHO algorithm in terms of better solution quality and lower fuel cost. The simulation studies verify that the proposed algorithm decreases the minimum operation costs by at least 0.1870%, 0.342%, 0.05224%, and 0.07875% compared to the best results in the literature.

Evolutionary Algorithm to Optimize Process Parameters of Al/Steel Magnetic Pulse Welding

The Magnetic Pulse Welding (MPW) process uses only electromagnetic force to create a solid-state metallurgical bond between a working coil and outer workpiece. The electromagnetic force drives the outer tube to collide with the inner rod, resulting in successful bonding. However, due to the dissimilarity of the MPW joint, only a portion of the interface forms a metallurgical bond, which affects the quality of the joint. Therefore, the purpose of this study is to analyze the effects of process parameters on joint quality through experimental work using RSM. Furthermore, an optimization algorithm is utilized to optimize the process parameters used in magnetic pulse welding. A1070 aluminum and S45C carbon steel were used as the materials, while peak current, gap between working coil and outer tube, and frequency were chosen as the process parameters for MPW. The welding conditions are determined through experimental design. After welding, the maximum load and weld length are measured to analyze the effect of the process parameters, and a prediction model is developed. Specifically, to achieve a high-quality joint, the process parameters are optimized using the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA). The results reveal that the peak current is a significant parameter, and the developed prediction model exhibits high accuracy. Furthermore, the ICA algorithm proves very effective in determining the process parameters for achieving a high-quality Al/Steel MPW joint.

Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability.

Develop an integrated candlestick technical analysis model using meta-heuristic algorithms

PurposeIn this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.Design/methodology/approachFurther as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.FindingsAs per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.Research limitations/implicationsIn this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.Originality/valueIn this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

A hybrid clustering-based type-2 adaptive neuro-fuzzy forecasting model for smart control systems

The practical applicability of smart control systems suffers from the existence of uncertainty in the physical parameters, sensor measurements and external excitations. Effective methods and its ease of implementation are required in the real-time active control strategies. To this end, in this research, we introduce an overlapping clusters and Multilayer Extreme Learning Machine (ML-ELM)-aided interval type-2 Takagi-Sugeno fuzzy inference system for vibration mitigation of smart structures. The combined Fuzzy C-Means (FCM) and imperialist competitive algorithm (ICA) with respect to degree of fuzzy overlap between clusters was utilized for characterizing the upper and lower membership functions forming the antecedent part. Furthermore, indeterminacy was modeled as footprint of uncertainty (FOU) and automatically formed based on statistical characteristics of soft clusters. The consequent parameters were adjusted by hybridization of ML-ELM and overlapping clusters, then the adaptivity was guaranteed. The predictive accuracy and generalization capability of the proposed method were demonstrated over the estimation of active control effort. Finally, to prove the practicability of the implemented framework, it was applied to four high-rise, mid-rise and low-rise benchmark building structures equipped with active mass damper (AMD), active tendon and active bracing system (ABS). The obtained results demonstrated the higher predictive accuracy and generalization ability of the presented data-driven control scheme compared to the other standard technical machine learning strategies. This practical guideline is easy to implement and automates control engineering tasks in real-time applications, solving one of the most challenging aspects of smart control systems.