Imperialist Competitive Algorithm Research Articles

Machine failures can generally be divided into soft failures caused by the normal usage of the equipment itself and hard failures caused by external impacts. Soft failures and hard failures may also affect each other, meaning that external impacts can accelerate the degradation of the equipment's performance and make it more prone to failures as the number of impacts increases. We introduce a new maintenance and reliability model which considers the dependencies between hard and soft failure modes for individual machine types, allowing hard failure thresholds to be dynamically adjusted. This model, based on a manufacturing system of general topology, not only incorporates degradation-shock dependence, but also includes a reliability constraint. The model is set up such that once the degradation level of any machine exceeds a predetermined threshold, maintenance and repair actions, both imperfect and perfect, will be executed. In order to determine the optimal thresholds for maintenance and upkeep strategy, we develop a simulation optimisation algorithm, known as the Jaya-Imperialist Competitive Algorithm (JICA). This method is based on the Imperialist Competitive Algorithm (ICA) and combines the Jaya Algorithm with an adaptive penalty function. Empirical analysis has confirmed that, compared to a set of benchmark algorithms, the algorithm proposed in this study can obtain a superior maintenance and repair strategy while demonstrating greater efficiency. We also perform sensitivity analysis on several important parameters, which can facilitate a deeper understanding of the optimisation model and support optimal decision-making.

Voltage instability is a growing concern in modern distribution networks with high penetration of wind energy due to the intermittent nature of wind generation. This study introduces an intelligent framework for the coordinated placement and sizing of fixed capacitors, switched capacitors, and static VAR compensators in radial distribution systems. The method integrates a hybrid evolutionary optimization technique, combining imperialistic competitive algorithm and particle swarm optimization (PSO), to identify optimal control strategies under varying load and wind conditions. Candidate locations for compensation devices are determined through loss sensitivity analysis, while a clustering mechanism based on PSO is used to group system states for efficient planning. The objective is to maximize operational efficiency by minimizing active power loss and improving voltage profiles, ultimately increasing economic returns. The proposed approach is validated on a 33-bus distribution test system, demonstrating improved performance compared to single-device allocation strategies.

Batch processing machines (BPMs) are extensively present in high energy-consuming manufacturing processes such as casting, and they show some symmetries on adjacent batches and jobs within each batch. Preventive maintenance (PM) is very important for the stable running and energy saving of BPMs; however, PM in a parallel BPM shop is seldom studied. In this study, the energy-efficient parallel BPM scheduling problem with PM is considered and an imperialist competitive algorithm with three empires (TEICA) is presented to minimize makespan and total energy consumption. To obtain high-quality solutions, the number of empires is not used as a parameter and fixed at 3, a new way is applied to construct three initial empires, each of which has a new structure like two imperialists, a new assimilation is given, and an adaptive imperialist competition is implemented based on historical competition data. A number of computational experiments are conducted on 108 instances. The computational results show that the new strategies of TEICA are effective; TEICA can provide better results than all comparative methods on more than 90% instances of the considered BPM scheduling problem, and TEICA may be an effective way to solve other BPM scheduling problem.

Radio-Frequency Energy Harvesting (RFEH) can be considered a promising solution for powering devices in the Internet of Things era, such as low-power wireless sensors, since RF electromagnetic waves are commonly found in diverse environments, due to different communication systems. To harvest the electromagnetic waves energy and convert them into Direct Current (DC), rectennas, which are composed of an antenna together with a rectifier, are used. However, the design of such rectifiers has two major challenges: the low spectral power density available and the dependence of circuit behavior on the operation temperature. To overcome the former, the Power Conversion Efficiency (PCE) of the circuit should be as high as possible, using, for instance, devices with low drop voltage. To improve the energy transfer from the antenna to the load, an Input Matching Network (IMN) is generally used. However, the rectifier input impedance varies with the temperature, which can degrade significantly the circuit performance. For this reason, to improve the circuit behavior, it would be desirable to have an RF design with low-temperature dependence. Schottky diodes are a common choice for RF rectifiers owing to their low conduction voltage and fast switching capability. Nevertheless, aiming to provide a better integration into commercial CMOS processes, such that the rectifier is placed together with the circuit it aims to power, it would be interesting to substitute Schottky with diode-connected MOSFETs. Therefore, this work aims to design rectifiers using a commercial RF 65nm CMOS process focusing on RFEH systems and considering the temperature influence.Two circuits were considered in this work: one basic Dickson charge pump, as presented in Fig. 1, and a 3-stage Dickson charge pump. In Fig. 1, VRF represents the RF source, M1 and M2 are the diode-connected transistors, CL is the load capacitance, and Cc is the coupling capacitance. In this circuit, the two diode-connected MOS operate alternately, one conducting at each half cycle of the input AC signal. Therefore, the voltage at CL is increased. For a 3-stage circuit, each basic cell of Fig. 1 is connected in cascade to the previous one. By using multiple stages, the output voltage can be further increased. However, there is a voltage drop at each device, in order to start its conduction, which can be a limiting factor for the circuit operation. For the determination of the devices width and length, an optimization tool incorporating the Imperialist Competitive Algorithm (ICA) has been used. The algorithm considers a Gaussian profile applied to the lower limit, center value, and upper limit fitness functions aiming to search for robust solutions regarding the variations of the manufacturing processes and environmental conditions. In the optimization process, the focus was a low temperature dependence while maximizing the output voltage. The TSMC RF CMOS 65nm PDK has been used, considering low threshold voltage transistors. The optimization was performed for the 3-stage circuit, whereas the one with just the first stage (parameters taken from the 3-stage optimization) was simulated for a comparison between their performances. A load of 1kW was considered, and the operation frequency was chosen as 2.45 GHz (ISM band).Fig. 2 presents the PCE as a function of the input power (Pin) for both circuits operating at different temperatures. It can be observed that the 1-stage circuit has provided a higher PCE than the 3-stage one. This can be understood by the fact that there are more transistors in the latter circuit and there is a voltage drop in each transistor, reducing the circuit efficiency. On the other side, the temperature has not affected the circuit behavior, which is significantly different from the rectifiers using Schottky diodes, in which the temperature increase results in a significant PCE reduction. In Fig. 3, the output DC voltage (Vout) obtained by these two circuits is presented as a function of the RF signal peak input voltage (Vin) at different temperatures. From this figure, it is clear that when increasing the number of stages, a higher output voltage can be obtained, making the 3-stage Dickson charge pump more adequate for supplying a low-power circuit. Nevertheless, a higher possible output voltage does not mean higher efficiency as previously demonstrated.In summary, in this work two MOS rectifiers, one with a single stage and a second one with 3 stages, were designed using commercial 65nm PDK aiming at RFEH applications operating at 2.45 GHz. The circuit has shown a low-temperature dependence. The higher the number of stages, the higher the output voltage. However, an efficiency reduction was observed for the multiple-stage rectifier. Figure 1

In order to increase survival rates and get treatment faster, early and accurate diagnostic systems are needed for cardiovascular disease (CVD), which is still a big worldwide health concern. In order to improve early identification of CVD, this study suggests an advanced ML framework that integrates various classification methods with a meta-heuristic feature selection method. The model uses an ensemble technique that combines K-Nearest Neighbor (KNN), Naive Bayes, and Support Vector Machine (SVM) classifiers. This allows the model to take use of each classifier's capabilities while reducing susceptibility to noisy data and overfitting. By using the Imperialist Competitive Algorithm (ICA) for feature selection, the study optimizes prediction accuracy by reducing data dimensionality while keeping crucial clinical information. A thorough evaluation of the model was guaranteed by preprocessing the medical dataset and dividing it in half for testing and training. The results show that when compared to individual classifiers, the proposed ICA-based multi-classifier ensemble obtains better accuracy, precision, recall, and F1-score. Cardiovascular decision support systems benefit from this hybrid approach because it increases diagnostic accuracy while also making them more trustworthy and easier to understand.

Abstract The integration of artificial intelligence (AI) into blasting operations has demonstrated significant improvements in precision, safety, and operational efficiency. By leveraging AI algorithms to optimize blast design, monitor field conditions, and analyze fragmentation data, more informed decision-making is achieved. In small-scale mining, safe and efficient blasting practices are critical to protecting workers, maximizing resource extraction, and minimizing environmental impacts. This study first reviews the applications, advantages, and limitations of various AI techniques used in predicting blast performance and environmental effects, with specific attention to the overlooked impact of multicollinearity and the absence of explicit mathematical expressions in many soft computing models. In the experimental section, an imperialist competitive algorithm (ICA)-optimized artificial neural network (ANN) model is developed to predict the percentage of oversized material produced by small-scale blasting. Field data, including blast parameters and rock strength, were collected from a dolomite quarry in Akoko, Edo State, Nigeria. Fragmentation analysis was conducted using WipFrag 4.0 software across 48 blast rounds, using the primary crusher gape as the decision threshold. Input parameter selection was guided by multicollinearity analysis to ensure robust modeling. Evaluation metrics such as root-mean-square error (RMSE), correlation coefficient (R²), mean absolute percentage error (MAPE), variance accounted for (VAF), Nash–Sutcliffe efficiency (NSE), and performance index (PI) confirmed that the ICA-ANN model significantly outperformed the standard ANN. While the conventional ANN model underestimated oversize by 14.7%, the ICA-ANN achieved a lower prediction error of 2.7%. The proposed model offers a practical and accurate tool for predicting oversized fragmentation in small-scale rock engineering scenarios, contributing to improved blasting efficiency and sustainability in the mining sector.

Efficient material handling is crucial in the logistics operations of modern salt warehouses, where Rail Guided Vehicles (RGVs) and Air Sorting Robots (ASRs) are often deployed to manage inbound and outbound tasks. However, as the number of tasks increases within a given period, conflicts and deadlocks between simultaneously operating RGVs and ASRs become more frequent, reducing efficiency and increasing energy consumption during transportation. To address this, the research frames the inbound and outbound problem as a task allocation issue for the RGV/ASR system with a finite buffer, and proposes a collision avoidance strategy and a zero-wait strategy for loaded machines to reallocate tasks. To improve computational efficiency, we introduce an adaptive multi-neighborhood hybrid search (AMHS) algorithm, which integrates a dual-sequence coding scheme and an elite solution initialization strategy. A dedicated global search operator is designed to expand the search landscape, while an adaptive local search operator, inspired by biological hormone regulation mechanisms, along with a perturbation strategy, is used to refine the local search. In a case study on packaged salt storage, the proposed AMHS algorithm reduced the total makespan by 30.1% compared to the original task queue. Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA.

Vehicular Ad Hoc Networks (VANETs) are essential components of modern intelligent transportation systems. Despite their advantages, VANETs face routing, network lifetime, and optimized energy consumption challenges due to their dynamic nature and high mobility. This paper introduces the ImCaG-Net model, a novel approach that addresses these challenges by combining the Imperialistic Competitive Algorithm (ICA) with Gated Recurrent Units (GRU). Comparative analysis of ImCaG-Net with existing models shows a 25% increase in network lifetime and a 30% increase in energy efficiency, confirming the effectiveness of the proposed model. The ImCaG-Net model demonstrates superior performance with an increase in packet transmissions to Cluster Heads (CHs) up to 60,000 and to Base Stations (BSs) up to 50,000 over 6000 rounds, highlighting its effectiveness compared to traditional models like LEACH and PEGASIS.

Destructive methods, though traditionally used to evaluate fruit safety, frequently do not deliver complete and detailed information. Non-destructive methods, especially spectroscopy, provide an effective solution for fast, efficient, and non-invasive assessments of quality and safety. This study utilized visible and near-infrared (Vis-NIR) spectroscopy to quantify the nitrate content in three cultivars of bell pepper—orange, yellow, and red—across a spectral range spanning 350 to 1150 nanometers. The nitrate content was assessed destructively, and spectral data were examined through partial least squares regression (PLSR). Model efficacy was measured using the root mean square error (RMSE) and coefficient of determination (R2). The R2 values, indicative of the model’s predictive efficacy, were determined to be 0.77, 0.85, and 0.81 for the yellow, red, and orange types, respectively. To optimize wavelength selection and improve model performance, a hybrid approach was utilized, integrating a support vector machine (SVM) with four meta-heuristic algorithms: particle swarm optimization (PSO), genetic algorithm (GA), imperialistic competitive algorithm (ICA), and ant colony optimization (ACO). The SVM-PSO approach proved to be the most efficient in pinpointing 15 key wavelengths. Following this, three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural network (ANN)—were utilized with the identified wavelengths. Among these, ANN represented the best performance, achieving validation R2 values of 0.99, 0.97, and 0.92 for the yellow, red, and orange varieties, respectively. Compared to traditional PLSR and MLR models, which reached validation R2 values up to 0.93, the ANN model demonstrated a significant improvement in prediction accuracy. This quantitative improvement highlights the advantage of combining hybrid meta-heuristic wavelength selection with ANN modeling. The results underscore the promise of visible/near-infrared (Vis/NIR) spectroscopy, integrated with sophisticated modeling approaches, as an effective non-invasive method for estimating nitrate concentrations in bell peppers. This technique represents a significant advancement in supporting food safety measures and quality assurance processes.

In response to the path planning problem of using Unmanned Aerial Vehicle (UAV) for blood transportation, with the objective of minimizing the total distance travelled by the UAV, a multi-constraint drone blood transportation path planning model is established. Taking into account the limited number of drone take-off and landing platforms and the safety time intervals for continuous drone take-off and landing, a drone take-off scheduling strategy is designed to reduce the total time spent by UAV completing transportation tasks. Additionally, an Imperial Competition Algorithm based on Imperialist Competitive Algorithm is proposed to solve this problem. This algorithm introduces a sine disturbance strategy and adds Imperial Reform stages to improve the search accuracy of the algorithm. It utilizes acceptance criteria related to solution quality to maintain the diversity of the population. Validation is conducted using benchmark examples and instances of drone blood transportation. The results indicate that the proposed algorithm can provide transportation solutions for drone blood transportation tasks that meet various constraints without any conflicts in drone take-off and landing. The drone take-off scheduling strategy effectively reduces the total time spent by UAV in completing tasks.

Small-Signal Model of GaN-on-Si HEMTs Based on Improved Imperialist Competitive Algorithm

Purpose: Brain-Computer Interfaces (BCI) are advanced systems that enable a direct neural pathway between the human brain and external devices. The importance of BCI is underscored by its profound implications for medical therapeutics, particularly in neurorehabilitation. Materials and Methods: This study developed an algorithm to detect 8 motion commands for a robot using individuals' EEG signals (Electroencephalogram). These signals were recorded during imagined and expressed commands. The research aimed to identify optimal features for extracting and classifying EEG signals for robot commands and to pinpoint the best EEG channels for a cost-effective, efficient signal acquisition system. Four categories of features, including temporal, frequency, wavelet, and combined features were extracted from the EEG signals. The Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA) were utilized for feature selection. Results: Findings revealed that wavelet features are most effective for analyzing and classifying EEGs. For imagined commands, optimal features from all channels achieved a 96.3% classification accuracy, while expressed commands reached 96.5%. The frontal and parietal lobes were identified as the prime EEG channels for command detection, achieving accuracies of 91.5% and 86.9% for imagined commands, and 92.7% and 86.1% for expressed commands, respectively. The result also indicated that the brain's midline and left hemisphere (containing the Broca area) outperformed the right hemisphere in classification. Conclusion: By focusing on the optimal EEG channels, a more cost-effective hardware system can be designed, surpassing the traditional 21-channel system and requiring only 14 electrodes in the frontal and parietal regions.

Although recommender systems (RSs) strive to provide recommendations based on individuals' histories and preferences, most recommendations made by these systems do not utilize location and time-based information. This paper presents a travel recommender system by integrating the Imperialist Competitive Algorithm (ICA) and Fuzzy C-Means (FCM) Clustering algorithm. Compared to similar studies, this recommender system takes into account more POIs, including location, number of visits, weather conditions, time of day, user mood, traffic volume, season, and temperature. The effectiveness and accuracy of the proposed method are assessed using the Flickr dataset, indicating that it is able to provide effective and accurate recommendations that are compatible with the user's interests and the current status of his/her visit. Results showed that, precision and Mean Absolute Precision (MAP) in the proposed method have been grown 23.6% and 23.72% in comparison to Popularity Rank, 28.98% and 19.67% in comparison to Classic Rank and 18.66% and 19.67% in comparison to Frequent Rank methods. Also, Mean Absolute Error (MAE) index in proposed method has been improved 60.71%, 64.51% and 56% in comparisons to the Popularity Rank, Classic Rank and Frequent Rank methods respectively.

Large-Scale Graph Signal Denoising: A Heuristic Approach

Optimizing energy use efficiency and environmental performance in cotton and canola production using the Imperialist Competitive Algorithm

ABSTRACT Excessive groundwater extraction in the Jakarta Metropolitan Region (JMR) has led to land subsidence, making the region more prone to flooding during heavy rain and at risk of being submerged by seawater during high tide. A reliable method for surface deformation measurements needs to be developed to address this issue. This article examines the use of multitemporal Sentinel-1 synthetic aperture radar (SAR) data from 2015 to 2023. It focuses on processing these data using the Improved Combined Scatterer Interferometry with Optimized Point Scatterers (ICOPS) method, integrated with the MintPy algorithm, to address instances of nonlinear deformation. The accuracy of the developed method was evaluated to that of the original ICOPS method using GPS data. According to the RMSE value, the ICOPS-MintPy (CBTU: 1.82 cm/year; CTGR: 0.93 cm/year; CJKT: 1.16 cm/year) outperformed the original ICOPS (CBTU: 1.99 cm/year; CTGR: 1.71 cm/year; CJKT: 1.93 cm/year) method. Thus, the mean deformation rate map obtained with the ICOPS-MintPy algorithm is suitable as an inventory map for identifying areas susceptible to future land subsidence around JMR. A convolutional neural network (CNN) and long short-term memory (LSTM) were utilized to generate susceptibility maps. In addition, metaheuristic algorithms were implemented to optimize the parameters of both CNN and LSTM. The metaheuristic algorithms are the gray wolf optimizer (GWO) and the imperialist competitive algorithm (ICA). We intend to employ a combination of deep learning and metaheuristic algorithms to create four hybrid models: CNN-GWO, CNN-ICA, LSTM-GWO, and LSTM-ICA. The performance of these hybrid models will be compared to that of standalone CNN and LSTM algorithms as the base model before parameter optimization. This process will be conducted using the area under the curve (AUC) value from the receiver operating characteristic (ROC) curve analysis. The susceptibility model performance of the LSTM-GWO has the highest AUC value of 0.976, followed by the CNN-GWO at 0.974, LSTM-ICA at 0.972, LSTM at 0.965, CNN-ICA at 0.960, and CNN at 0.951. Nevertheless, all of the models have excellent performance, as shown by the AUC values between 0.9 and 1.0. Finally, the ICOPS-MintPy algorithm for land subsidence monitoring and hybrid deep learning algorithms for susceptibility mapping resulted in more accurate results due to its improved accuracy.

With the increasing volume of scientific computation data and the advancement of computer performance, scientific computation is becoming more dependent on the powerful computing capabilities of cloud computing. On cloud platforms, tasks in scientific workflows are assigned to computational resources and executed according to specific strategies. Therefore, workflow scheduling has become a key factor affecting efficiency. This paper proposes a hybrid scientific workflow scheduling algorithm, HICA, to address the scheduling problem of scientific workflows in symmetric homogeneous cloud environments with optimization goals of makespan and cost. HICA combines the Imperialist Competitive Algorithm (ICA) with the HEFT algorithm, integrating HEFT into the initial population of the ICA to accelerate the convergence of the ICA. Experimental results show that the proposed hybrid approach outperforms other algorithms in real-world workflow applications. Specifically, when the workflow scale is 100, the average improvements in makespan and cost are 133.89 and 273.33, respectively; when the workflow scale is 1000, the improvements are 371.62 and 9178.98. The scheduling results for the Earth System Model parameter tuning workflow show that compared to the scenario without using a scheduling algorithm, the makespan and cost were improved by 13% and 21%, respectively.

Recently, maximum reservoir contacting (MRC) wells have attracted more and more attention and have been gradually applied to CO2 WAG injections. During the use of MRC wells for CO2 WAG injections, intelligent completions are commonly considered to control CO2 breakthroughs. However, the design of the operational and intelligent completion parameters is a complicated process and there are no studies on the co-optimization of the operational and intelligent completion parameters for CO2 WAG processes. This study outlines an approach to enhance the oil recovery from CO2 WAG injection processes through the co-optimization of the operational and intelligent completion parameters of MRC wells in a carbonate reservoir. First, a simulation method is developed by using Petrel and Intersect. Then, a series of simulations are performed to prove the viability of intelligent completions and to investigate the effects of the timing and duration of the CO2 WAG injection, as well as the type, number, and placement of intelligent completion devices on the performance of a CO2 WAG injection by MRC wells. Finally, the imperialist competitive algorithm is used to co-optimize the operational and intelligent completion parameters for MRC wells. The results show that compared with the spiral inflow control device (SICD), autonomous inflow control device (AICD), labyrinth inflow control device (LICD), and annular interval control valve (AICV), the nozzle inflow control device (NICD) is the best type of intelligent completion device for MRC wells. There is an optimal installation timing, inflow area, and number of NICDs for a CO2 WAG injection by MRC wells. The NICDs need to be placed based on the permeability distribution. The oil recovery for the optimal case with the NICDs reached 46.43%, which is an increase of 3.8% over that of the base case with a conventional completion. In addition, compared with the non-uniformity coefficient of the base case (11.7), the non-uniformity coefficient of the optimal case with the NICDs decreased to 4.21. This is the first time that the co-optimization of the operational and intelligent completion parameters of a CO2 WAG injection has been reported, which adds more information about the practical applications of MRC wells in CO2 WAG injections for enhancing oil recovery in carbonate reservoirs.

Currently, the time-of-use pricing model for electricity focuses on a single objective, often overlooking various factors that influence electricity costs. This oversight can lead to significant disparities in peak and off-peak electricity usage within the distribution network following optimization. Therefore, a new time of using electricity price optimization method is proposed that takes into account the losses of distributed photovoltaic access to the distribution network. Considering the topology structure of the distribution network after the integration of distributed photovoltaic, this paper calculates the comprehensive losses generated by the operation of the distribution network. Also, this paper constructs a time of use electricity price optimization mathematical model with the objectives of minimizing network loss, minimizing load variance, minimizing peak valley difference of equivalent load, and maximizing user satisfaction. And refer to the basic requirements for electricity pricing in the distribution network, set a series of constraints for optimizing electricity prices. Applying an improved imperialist competition algorithm this paper integrates Tent chaotic reverse learning to solve a multi-objective optimization model and obtain an optimized time of use electricity pricing plan. The experimental results show that after the implementation of this optimization method, the peak valley difference of the daily power load curve of the distribution network is only 350 MW, demonstrating superior peak shaving and valley filling effects.

Properly locating these facilities is a substantial factor in the success of the logistics systems. In this paper, a bi-objective mathematical model for a maximal covering hub location problem is presented to minimize time and environmental risks. The Goal Attainment method was employed to solve the small-sized problems for model validation. Since the problem is NP-Hard, the Multi-Objective Imperialist Competitive Algorithm (MOICA) meta-heuristic algorithm was exploited for solving the medium and large-sized problems. The performance of MOICA was compared with the performance of the Goal Attainment method and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to validate the proposed model and solution approach. This paper can direct the logistics companies to reduce the cost, time, and environmental effects of their transportation networks. In addition, this research can optimize energy consumption in the transportation sector for the continuation of low-cost services and reduce fuel consumption, which leads to reducing environmental pollution.