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In 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.

Further 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.

As 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.

In 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.

In 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.

The complexity and interdisciplinarity of healthcare industry problems make this industry one of the attention centers of computer-based simulation studies to provide a proper tool for interaction between decision-makers and experts. The purpose of this study is to present a metamodel-based simulation optimization in an emergency department (ED) to allocate human resources in the best way to minimize door to doctor time subject to the problem constraints which are capacity and budget.

To obtain the objective of this research, first the data are collected from a public hospital ED in Brazil, and then an agent-based simulation is designed and constructed. Afterwards, three machine-learning approaches, namely, adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FNN) and recurrent neural network (RNN), are used to build an ensemble metamodel through adaptive boosting. Finally, the results from the metamodel are applied in a discrete imperialist competitive algorithm (ICA) for optimization.

Analyzing the results shows that the yellow zone section is considered as a potential bottleneck of the ED. After 100 executions of the algorithm, the results show a reduction of 24.82 per cent in the door to doctor time with a success rate of 59 per cent.

This study fulfils an identified need to optimize human resources in an ED with less computational time.

The purpose of this study is to optimize the location of hospitals in Gorgan, Iran, to provide desirable services to citizens in the event of an earthquake crisis.

This paper, due to target, is practical and developmental, due to doing method is descriptive and analytical and due to information gathering method is documental and surveying. In the present study, the capabilities of genetic algorithms and imperialist competition algorithm in MATLAB environment in combination with GIS capabilities have been used. In fact, cases such as route blocking, network analysis and vulnerability raster have been obtained from GIS-based on current status data, and then the output of this information is entered as non-random heuristic information into genetic algorithms and imperialist competition algorithm in MATLAB environment.

After spatial optimization, the hospital service process has become more favorable. Also, the average cost and transfer vector from hospitals to citizens has decreased significantly. By establishing hospitals in the proposed locations, a larger population of citizens can access relief services in less time.

Spatial optimization of relief centers, including hospitals, is one of the issues that can be of significant importance, especially in the event of an earthquake crisis. The findings of the present study and the originality, efficiency and innovation of the used methods can provide a favorable theoretical framework for the success of earthquake crisis management projects.

The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities.

The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs.

As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time.

Predictive maintenance (PM) activities carry out the operations on machines and tools before the breakdown takes place and it helps to prevent failures before they happen.

The purpose of this paper is to optimize a two-level perishable product supply chain by increasing its revenue with the Internet of Things (IoT). It particularly investigates how radio-frequency identification (RFID) technology impacts the revenue of the supply chain.

In this paper, two revenue models were built to calculate the revenue of perishable product supply chain before and after the application of IoT to analyze the influences of IoT on perishable product supply chain. In the case study, particular data of an aquatic product supply chain were analyzed through these models which were later solved by a computer simulation method based on Colonial Competitive Algorithm, a new heuristic algorithm inspired by imperialistic competition in human society.

Using these revenue models to compare the revenues of supply chain before and after the application of IoT, this paper concludes that the application of IoT can efficiently optimize a perishable product supply chain by balancing its wholesale profits and its total costs including logistics costs, therefore, increasing its overall revenue. However, this conclusion is only applicable for large enterprises, while small enterprises are not supposed to introduce IoT due to its high cost.

The revenue models built in this paper can be used to evaluate the profits of supply chain and help enterprises determine how to maximize their profits and whether they should introduce IoT in a perishable product supply chain. In addition, through the analysis of case study, this paper gives several valuable suggestions to help enterprises reduce their logistics costs and increase their overall revenue.

The purpose of this paper is to present, an optimization problem based on the imperialistic competitive algorithm (ICA) approach for optimizing the needle velocity and variation of needle acceleration in a link drive mechanism of a sewing machine. The optimal geometry of the link drive has been achieved using a non-linear optimization procedure.

As an important study in this case, the authors might refer to a previous work in which they introduced the possibility of replacing the slider-crank mechanism, that is typically used in sewing machines, with a link drive mechanism. The authors regenerate the optimization problem by modifying the objective function and follow a novel optimization method based on the ICA to overcome the drawbacks of that work. In addition, further modification of the objective function with respect to the variation of needle acceleration is applied to assure smooth movement of the needle during sewing process.

The results showed a significant improvement with respect to the optimization of needle velocity and variation of needle acceleration in comparison to that previous work. This clearly justifies the efficiency and reliability of the optimization formulation based on the ICA approach.

Needle temperature is considered as an effective parameter on sewing process efficiency and stitch quality. Needle heat generated during sewing process is directly related to needle velocity in penetration zone which in turn depends on the needle driver mechanism of sewing machine. According to literature survey, few researches have focussed to design a driver mechanism of the sewing machine to reduce the generated needle heat. This mechanism has the ability of reducing the penetration velocity of the needle without affecting sewing speed which consequently can reduce the needle heat generated during needle penetration. The work here is novel regarding implementation of optimization algorithm for this mechanism.

The aim of this study was to determine the number of shelters, specify some optimal paths among building blocks towards shelters, and assign population to shelters.

Imperialist competition algorithm (ICA) and particle swarm optimization (PSO) were used to optimize the objectives of this study.

The optimal value for PSO objective function was with the number of function evaluations (NFE) of 5300 and the optimal value of ICA objective function was with NFE of 1062. Repetition test for both algorithms showed that imperialist competition algorithm enjoys better stability and constancy and higher speed of convergence compared to particle swarm algorithm. This has been also shown in larger environments. 92% of the existing populations have access to shelters at a distance of less than600 meters. This means that evacuation from the building blocks to shelters takes less than 8 minutes. The average distance from a block (for example, a residential complex) to an optimal shelter is approximately273meters. The greatest risk of route and shelter has been 239 and 121, respectively.

To address these goals, four following objective functions were considered: a) minimization of the distance for getting all the people to shelters b) the lowest total risk of the discharge path c) minimization of the total time required to transfer people to shelters or hospitals if necessary, and d) the lowest total risk in shelters.

Over the recent decades, the frequency of so-called ‘natural’ disasters has increased significantly worldwide and resulted in escalating human and economic losses. Among them, the earthquake is one of the major concerns of the various stakeholders related to urban planning.

In addition, the maximum time of discharge from the helter to the hospital has been 17 minutes, which means the presence of good access to selected shelters.

The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a crucial role in the evaluation process to improve the training process performance. This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’ characteristics. The employees are classified into following categories: necessary training, required training, and no training. The work may answer the question of how to spend the budget of training for the employees. This investigation presents the use of fuzzy optimization and clustering hybrid model (data mining approaches) as a fuzzy imperialistic competitive algorithm (FICA) and k-means to find the employees’ categories and predict their training requirements.

Prior research that served as an impetus for this paper is discussed. The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions.

This paper focuses on how to find a good model for the evaluation of employee profiles. The paper introduces the use of artificial intelligence methods (fuzzy optimization (FICA) and clustering techniques (K-means)) in management. The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses. Finally, the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements.

This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges (in TCT for the first time).

The purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.

For this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.

After modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with the R2 of 0.939 was the most precise model for predicting the PPV in the present study.

In the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

The purpose of this study is truck scheduling and assignment of trucks to the doors simultaneously since these issues were considered mainly separately in the previous research. Also, the door service time and its impact on truck scheduling were not taken into account, so this research endeavors to cover this gap.

In this research, a novel model has been presented for simultaneous truck scheduling and assignment problem with time window constraints for the arrival and departure of trucks, mixed service mode dock doors and truck queuing. To resolve the developed model, two meta-heuristic algorithms, namely, genetic and imperialist competitive algorithms, are presented.

The computational results indicate that the proposed framework leads to increased total costs, although it has a more accurate planning; moreover, these indicate that the proposed algorithms have different performances based on the criteria considered for the comparison.

There are some limitations in this research, which can be considered by other researchers to expand the current study, among them the specifications of uncertainty about arrival times of inbound and outbound trucks, number of merchandises which has been loaded on inbound trucks are the main factors. If so, by considering this situation, a realistic scheme about planning of cross docking system would be acquired. Moreover, the capacity of temporary storage has been considered unlimited, so relaxing this limitation can prepare a real and suitable situation for further study. Examining the capacity in the front of each type of doors of cross-dock and executive servers are the other aspects, which could be expanded in the future.

In this study, a mathematical programing model proposed for truck scheduling to minimize total costs including holding, truck tardiness and waiting time for queue of trucks caused by the interference of each carrier’s movement. At the operational levels, this research considered a multi-door cross-docking problem with mixed service mode dock doors and time window constraints for arrival and departure time of trucks. Moreover, M/G/C queue system was developed for truck arrival and servicing of carriers to trucks.