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Optimization plays a crucial role in the elevator group control system (EGCS) since various unpredictable factors, such as future traffic demand of each floor, passengers' random destinations, and indiscriminate starting-stopping of elevators, are incorporated in scheduling a group of elevators. When solving the optimization problem of EGCS, a number of dynamic performance indices, including average waiting time, average journey time, energy consumption, etc., have to be taken into account. Until now, numerous optimization approaches have been utilized to solve the car-dispatching problem of vertical transportation. Among those methods, in this study, the authors concentrate on various metaheuristic techniques that were implemented to optimize the metrics of EGCS. While establishing a metaheuristic approach, all the previous authors recognized various factors and limitations, which ought to analyze to develop a new metaheuristic-based EGCS. Owing to this, EGCS implemented via metaheuristic techniques is summarized in this review study, together with the underlying contributions, fitness functions, computational time, and limitations. What is more, performance comparisons of different previously implemented metaheuristic approaches are depicted in this study. This research will not only assist to figure out optimal elevator group optimization algorithms, but also will shrink the technological gap by outlining a number of potential future research lines and methodologies. [ABSTRACT FROM AUTHOR]

This study explores the applicability of the metaheuristic algorithm for calculating the building period. Firefly algorithm (FA) employing the feed-forward (FF) model is applied to an existing period dataset of several wood buildings, whose motions were recorded at ambient conditions. The composition of the considered artificial neural network (ANN) is optimized employing the firefly algorithm, and the fixed weights are determined with the least error for the model. The model's precision is evaluated by comparing the results with the multiple linear regression (MLR) model, the ANN model combined with the genetic algorithm (GA-ANN), particle swarm optimization algorithm (PSO-ANN), and the models existing in building code. The results are also compared with the models presented in the literature for the period estimate required for the seismic design of wood buildings. The paper concludes that the proposed informational model can predict the fundamental period of light-frame wood buildings more accurately than the other available models. [ABSTRACT FROM AUTHOR]

Meta-heuristics have been successfully applied to many complex optimization problems. One of the main reasons for its success is its ability to handle non-convex, nonlinear, multimodal, multi-variable, and multi-objective problems with easy implementation. However, the quality of the response of these algorithms to an optimization problem is highly susceptible to the control parameters, and few works aim to tune them or find tools that can improve the algorithms. The literature is rich in proposals for new algorithms, but not for improving existing ones. This paper presents different strategies for tuning and accelerating meta-heuristics using the first hybrid algorithm in the literature. The Lichtenberg algorithm is inspired by lightning and Lichtenberg figures and has been increasingly successfully applied to various optimization problems. However, a study of its best parameters has never been presented until now. After a discussion of the best tuning tools, its tuning parameters are performed using response surface methodology. Then, 14 versions are studied through 10 test functions using chaos theory and Lévy flights scenarios. After 13,500 simulations, the chaotic Lichtenberg algorithm equipped with the piecewise function and tuned parameters proved the best version with only 16% similarity to the original algorithm. Then, it was compared to the genetic algorithm, particle swarm optimization, gray wolf optimizer, salp swarm optimization, whale optimization algorithm, and dragonfly algorithm. The proposed algorithm had both the best average accuracy, lower computational cost, and the smallest standard deviation. [ABSTRACT FROM AUTHOR]

This research study proposes a Q-learning-based metaheuristic algorithm framework for thermal design optimization of a shell-and-tube evaporator operating with different refrigerant mixtures, which is a highly complex real-world design problem and has not been investigated yet, in previous literature approaches before. The proposed method, called QL-HEUR, uses Q-learning as a high-level heuristic to iteratively guide the competitive recently emerged low-level metaheuristic algorithms. QL-HEUR is applied to 32 unconstrained optimization benchmark functions, and results are evaluated in statistical analysis. Moreover, three multidimensional constrained optimization problems will be solved. Respective solutions unravel that QL-HEUR is very effective in finding optimum solutions to constrained and unconstrained optimization problems. QL-HEUR is employed on the design optimization of a shell-and-tube heat exchanger running with different mixture pairs as a challenging real-world benchmark case. For the design case in which R134a–R1234yf (0.8:02) mixture is considered, 8.71% of the total cost is saved compared to the preliminary design of a heat exchanger operated with pure R1234yf refrigerant. For the second design case, the application of QL-HEUR results in a decrease of 8.93% for refrigerant composition R32–R134a (0.6:0.4) in comparison with the configuration running with pure R134a. It is also seen that the heat exchanger configuration running with pure R32 refrigerant yields the lowest total cost compared to the cases accomplished by varying mixture ratios of R290 and R32. It can be concluded that the optimum configuration of the heat exchanger operated with a refrigerant mixture can be conveniently employed for minimum total cost and global warming potential. [ABSTRACT FROM AUTHOR]

Due to the users' mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of the network and closer to mobile users. Considering the moving nature of users and the heterogeneous service demands in different areas, the optimal placement of servers plays an important role in increasing the quality of service provided to users. However, because of the large number of servers, finding the optimal location of these resources is a serious challenge. In the proposed method of this paper (MES-WG), in the first step, the geographical area of server deployment is divided into smaller sub-regions to reduce the complexity of the problem. Then, by using the WOA algorithm the search agent finds the optimal location of the servers. In the next step, a neural network is used for the local placement of all servers in each area. Finally, game theory is deployed for the convergence of resource placement in all sub-regions. The experimental results show that the proposed method reduces the network latency by 33.5% and also improves the load balance on servers by 28.2%, compared to some of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Real-parameter single objective optimization has been studied for decades. In recent, a new setting is applied in this field based on the consideration that solving difficulty scales exponentially with the increase in dimensionality. Under the new setting, differential evolution (DE) still outstands in performance as before. Meanwhile, a new type of population-based metaheuristic—gaining–sharing knowledge-based algorithm, becomes a dark horse. Furthermore, ensemble of the above two types of algorithm is proposed in the literature. Although such ensemble shows good performance, provided that a more reasonable scheme is used for the communication between the constituent algorithms, better ensemble can be obtained. We believe that the new scheme should be with adaptiveness. In this paper, we propose an adaptive scheme for the communication. According to the scheme, individuals chosen based on fitness and lifetime are exchanged. In fact, in the field of DE, it is rare to consider lifetime of individual. However, lifetime is no less important than fitness in our scheme. In our experiment, our ensemble is compared with seven state-of-the-art algorithms. According to experimental results, our ensemble is comparable to one of the peers and better than the other ones. [ABSTRACT FROM AUTHOR]

For multi-threshold segmentation of grayscale and color images, the computational complexity increases exponentially with the increase of the number of threshold levels. In this paper, we propose a new method to segment grayscale and color images with Kapur entropy as the objective function. The method introduces four strategies such as horizontal crossover and vertical crossover into the bald eagle search optimization algorithm, forming an advanced bald eagle search optimization algorithm (ABES). In the benchmark function comparison experiments at IEEE CEC 2017, ABES was compared with classical and novel algorithms, and the results proved to have stronger convergence speed, convergence accuracy, and stability than other algorithms. To demonstrate the effectiveness of the method in multi-thresholding segmentation of grayscale and color images, it is applied to low-level and high-level image multi-thresholding experiments, and the experimental results show that ABES outperforms other algorithms in the evaluation of PSNR, MSSIM, and FSIM, and ABES is a high-quality image segmentation method. [ABSTRACT FROM AUTHOR]

This article discusses the intermittent nature of renewable energy sources and electric vehicles for improved load frequency control mechanism of interconnected power system. This research also looks at the impact of communication latency on system stability and sustainability. The mathematical modelling and analysis of the studied proposed system, a two-area interconnected hybrid power system, includes a thermal plant, hydro plant, and gas plant in each area. Moreover, both control areas also incorporate sporadic solar and wind power plants as well as electric vehicles for case study purposes. A novel cascade fractional-order integral-derivative and tilt controller has been designed for the analysed system. A new modified Quasi-Opposition Reptile Search Algorithm (QORSA) is also proposed to optimise the different parameters of controller. To demonstrate its superiority, the QORSA is compared with some recent prominent meta-heuristic algorithms. Furthermore, the effectiveness and efficacy of the proposed control approach has been analysed in comparison with existing controllers as well as some other research work under diverse conditions like step and random disturbances. The comprehensive results studies show the overall better and improved dynamic performance analysis for anticipated hybrid power system. Finally, the proposed methodology is validated through real-time experimental analysis by means of Opal-RT platform for its practical feasibility. [ABSTRACT FROM AUTHOR]

It is well recognized that human societies and their problem-solving capabilities have evolved much faster than biological evolution. The inspiration from human behaviors, knowledge exchange, and transformation has given rise to a new evolutionary computation paradigm. Multiple research endeavors have been reported in the literature inspired by the diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in piles of algorithms, worded differently but with more or less similar underlying mechanisms causing immense confusion for a new reader. This paper presents a generalized framework for these socio-inspired evolutionary algorithms (SIEAs) or socio-inspired meta-heuristic algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description and the comparison of each SIEA with the general framework enable a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. This paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research. [ABSTRACT FROM AUTHOR]

In this work, we intend to propose multiple hybrid algorithms with the idea of giving a choice to the particles of a swarm to update their position for the next generation. To implement this concept, Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Whale Optimization Algorithm (WOA) have been utilized. Exhaustive possible combinations of these algorithms are developed and benchmarked against the base algorithms. These hybrid algorithms have been validated on twenty-four well-known unimodal and multimodal benchmarks functions, and detailed analysis with varying dimensions and population size is discussed for the same. Further, the efficacy of these algorithms has been tested on short-term electricity load and price forecasting applications. For this purpose, the algorithms have been combined with Artificial Neural Networks (ANNs) to evaluate their performance on the ISO New Pool England dataset. The results demonstrate that hybrid optimization algorithms perform superior to their base algorithms in most test cases. Furthermore, the results show that the performance of CSA-GWO is significantly better than other algorithms. [ABSTRACT FROM AUTHOR]

Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several approaches have been proposed in the literature based on spatial and fr uency domain techniques. Among them, the Incomplete Beta Function (IBF) is a popular scheme used for image contrast enhancement. In the IBF based image contrast enhancement technique, quality of an image is improved by two controlling parameters. Under such conditions, these parameters need to be tuned for obtaining better outcomes. In this paper, a new gray-scale contrast enhancement algorithm is introduced where, instead of tuning the controlling parameters of IBF experimentally, their near-optimal values are calculated with the help of a recently published meta-heuristic algorithm called Artificial Electric Field Algorithm (AEFA). The proposed method has been compared with many state-of-the-art techniques in terms of some standard metrics considering three different datasets, namely Kodak, MIT-Adobe FiveK and USC-SIPI. Simulation results demonstrate that the proposed AEFA based image enhancement technique increases the overall image contrast and enriches the information present in the image. [ABSTRACT FROM AUTHOR]

Disasters are large-scale disruptions for the society that cause damage to human lives, infrastructure, and the ecosystem. Multiple emergencies emerge, which need to be handled and resolved efficiently to minimize the impact of the disaster. Therefore, the need for an effective and efficient emergency response system that optimally allocates resources and emergency services is essential. This paper proposes a methodology to model this situation as an optimization problem that can be solved using meta-heuristic algorithms. In the proposed model, four meta-heuristics, namely Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, and iLSHADE-RSP algorithm, have been used to allocate resources and services. A benchmark dataset consisting of 16 situations is prepared to analyze the proposed model. The conducted empirical analysis demonstrates the applicability of the meta-heuristic algorithms in locating near-optimum solutions for the considered situations. The convergence analysis and statistical tests have been performed to test the validity and significance of the conducted experiments. [ABSTRACT FROM AUTHOR]

The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms' ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration–exploitation stages during the search process; moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the proposed algorithm in avoiding local minima and increasing the convergence rate. The experimental result are compared with state-of-the-art algorithms, i.e., ALO, STOA, PSO, GWO, HHO, MFO, and MPA and found that the mean accuracy achieved is in range 0.94–1.00. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop and test three meta-heuristic algorithms to solve large size multi-objective supply chain network design problems. The algorithms are based on tabu search, genetic algorithm, and simulated annealing to find near-optimal global solutions. The three algorithms are designed, coded, and tested, and their parameters are fine tuned. The exact ε-constraint algorithm embedded in the General Algebraic Modeling System is used to validate the results of the three algorithms. The algorithms are compared using a typical multi-objective supply chain model utilizing several performance measures. The measures include the mean ideal distance, diversification metric, and percent of domination, inverted generational distance, and computation time. The results show that the tabu search algorithm outperformed the other two algorithms in terms of the percent of domination and computation time. On the other hand, the simulated annealing solutions are the best in terms of their diversity. The work in this paper is expected to help managers to solve large-scale supply chain problems that arise in oil and gas, petrochemical, and food supply chains. [ABSTRACT FROM AUTHOR]

Metaheuristic intelligent optimization algorithm is an effective method to settle high-dimensional nonlinear complicated optimization problems. Slime mould algorithm is a novel intelligent optimization algorithm proposed in 2020. However, the basic slime mould algorithm still has shortcomings, such as slow convergence rate, easy falling into local extremum, and imbalanced exploration and exploitation capabilities. To further enhance the optimization capability and expand the application scope of the slime mould algorithm, a slime mould algorithm based on the mechanism of multi-strategy information interaction and optimally oriented initialization (MSII-SMA) is proposed. Three improved mechanisms are introduced into the algorithm and the time complexity of MSII-SMA is analyzed. To verify the optimization effect, MSII-SMA and the other 5 typical comparison algorithms are applied to settle the CEC2015 test function set. The analysis of the optimization accuracy, convergence curve, Friedman test, boxplot and scalability test shows that the optimization ability, convergence rate, stability and scalability of MSII-SMA are evidently better than the comparison algorithm. Finally, MSII-SMA and other comparison algorithms are used to settle engineering design optimization problems with different complexity. The experimental results verify the universality, reliability and preponderance of MSII-SMA in dealing with engineering design constraint optimization problems. [ABSTRACT FROM AUTHOR]

Enhanced index tracking (EIT) problem is concerned with selecting a tracking portfolio to beat the benchmark on return while having the minimum tracking error. This paper addresses the EIT problem based on uncertainty theory where stock returns are treated as uncertain variables instead of random variables. Under the framework of uncertainty theory, the paper proposes a new uncertain EIT model where the higher-order moment of the downside is used as the tracking error measure, as higher-order moment makes the model more widely applicable and the downside risk is in line with investors' perception of risk. Besides, some realistic constraints are considered in the new uncertain EIT model. Then, the properties of the proposed model are discussed. To solve the model, we proposed, which is a nonlinear integer programming problem, a meta-heuristic algorithm presented. The efficiency of the algorithm and the applications of the proposed model are illustrated through numerical experiments. [ABSTRACT FROM AUTHOR]

Estimating market demand for a product is essential to business growth; yet, it is exceedingly difficult. This article considers various demand patterns for a single product in a three-echelon supply chain where a single supplier, a single manufacturer and a single retailer are involved. A robust inspection process is considered at the manufacturer's end to deliver a high-quality product to the retailer. The backorder strategy is incorporated to enhance the company's trust and reputation among consumers. The major idea of this research is to analyze the nature of the product's demand in three distinct situations, as well as the benefits of implementing the backorder technique. Three cases are analyzed on the basis of different demand patterns, in which the demand rate is assumed to be constant (i.e., deterministic), price-dependent and fuzzy in the first, second and third cases, respectively. To verify this model, numerical examples are tested independently with identical information for each case and owing to the complexity of the objective function; a genetic algorithm is employed to obtain the best solution. Finally, sensitivity analysis, managerial insights and conclusions, including the future directions, are provided. [ABSTRACT FROM AUTHOR]

Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm [ABSTRACT FROM AUTHOR]

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly. [ABSTRACT FROM AUTHOR]

Channel selection is difficult for electroencephalographic (EEG) channel data. The test for confidential information is performed using a 16-channel electrode and recorded EEG data. The system's performance suffers when optimal channel selection data are not available. Removing noise and irrelevant channels improves the outcomes and implementation of the EEG deception identification system. Swarm optimization techniques are frequently used to overcome feature selection problems. Transfer functions need swarm algorithms because they change the search space from one that is continuous to one that is discrete. The currently used sigmoid transfer function cannot balance exploration and exploitation stages in the search space, including optimal local problems. The novel binary Harris Hawks optimization with a time-varying transfer function (TVk-BHHO) is used in this work to select channels from EEG channel data optimally. Controlling the time-varying parameter is essential for balancing the search space's exploration and exploitation stages and avoiding the optimum local problem. In this case, a specific classifier from a random forest improves the system's performance and accuracy. The results show that TVk-BHHO has consistent channel selection, resulting in higher classification accuracy and more harmonious convergence. Existing meta-heuristic algorithms are against the simulation results of the new TVk-BHHO approach. The best accuracy achieved here is 98.50%. [ABSTRACT FROM AUTHOR]

In this study, the permutation flow shop scheduling problem with makespan criteria is investigated, and two algorithms, Jaya and SAMP-Jaya, are proposed. Jaya is a simple and effective meta-heuristic optimization technique that has been successfully applied in various engineering problems, while SAMP-Jaya is a modified version of Jaya that uses a multi-population approach. Both algorithms are parameter-less, and the ANOVA experiment is used to identify the significant control parameters. The population size is found to be the most crucial parameter for improving solution quality. The performance of Jaya and SAMP-Jaya is compared to the exact solution and the genetic algorithm, demonstrating the superiority of the proposed algorithms. The computational results confirm that the suggested optimization techniques are effective in terms of solution quality. [ABSTRACT FROM AUTHOR]

This paper introduces a new meta-heuristic technique, named geometric mean optimizer (GMO) that emulates the unique properties of the geometric mean operator in mathematics. This operator can simultaneously evaluate the fitness and diversity of the search agents in the search space. In GMO, the geometric mean of the scaled objective values of a certain agent's opposites is assigned to that agent as its weight representing its overall eligibility to guide the other agents in the search process when solving an optimization problem. Furthermore, the GMO has no parameter to tune, contributing its results to be highly reliable. The competence of the GMO in solving optimization problems is verified via implementation on 52 standard benchmark test problems including 23 classical test functions, 29 CEC2017 test functions as well as nine constrained engineering problems. The results presented by the GMO are then compared with those offered by several newly proposed and popular meta-heuristic algorithms. The results demonstrate that the GMO significantly outperforms its competitors on a vast range of the problems. Source codes of GMO are publicly available at https://github.com/farshad-rezaei1/GMO. [ABSTRACT FROM AUTHOR]

Hybrid renewable power generation becomes essential in most of electric power networks. Battery storage is commonly used in renewable energy systems (RESs) with distributed generation, such as solar and wind energy systems, to reduce power fluctuations caused by the intermittent behavior of renewable energy sources. A battery has been connected with the dc grid through a dc–dc bi-directional converter. The control scheme of the dc–dc bi-directional converter (BDC) is having two controlled loops. One is voltage-controlled loop, and another is the current-controlled loop. In this work, two-degree-of-freedom fractional order proportional integral derivative + proportional integral controller (2-DOF FOPID + PI) is proposed and applied in the voltage-controlled loop to stabilize the dc bus voltage and power flow effectively for both islanded mode and grid-connected mode. A novel meta-heuristic algorithm, i.e., modified sine cosine algorithm (m-SCA), is used to tune the controller parameters of the proposed controller. To test the performance of the modified SCA algorithm, various well-known uni-modal and multi-modal benchmark functions are taken in this paper. To confer the viability of the proposed controller, its performance is compared with other literature-based conventional controllers. This paper presents time-domain analysis, stability, and robustness test to illustrate the effectiveness of the proposed controller. MATLAB/Simulink has been used for computational work to analyze the system's efficiency. Finally, the proposed technique is validated by using OPAL-RT platform (Opal-RT-OP4510 model) with the power management of RES (PV and wind) and battery energy storage. Experimental work proved the effectiveness of the proposed m-SCA optimized 2-DOF FOPID + PI controller. [ABSTRACT FROM AUTHOR]

A novel multi-layer multi-spiking neural network (MMSNN) model sends information from one neuron to the next through multiple synapses in different spikes. When a suitable training method is unavailable, there are numerous complications in training spike neural networks. The convergence rate is slow when gradient descent algorithms are trained on multi-spike neural networks. Meanwhile, the spiking network is likely to perform poorly in classifying electroencephalographic (EEG) datasets due to its high propensity to fall within the local minima. With the help of a 16-channel electrode, the Concealed Information Test created the EEG dataset. The Mini Batch Stochastic Gradient Descent (MBSGD) built the batch size and cut the number of iterations to speed up the convergence rate. It can, however, avoid the problem of moving into the local minimum more effectively. This paper describes an MBSGD algorithm for MMSNN model training that addresses local minima and convergence rate issues. This paper presents an MBSGD-MMSNN model for classifying EEG data. The MBSGD algorithm adjusts the weight parameters of the MMSNN model based on the batch size until the error is less than the required value. As a result, we achieve a faster convergence rate and avoid issues with local minima. The performance of the proposed MBSGD-MMSNN model is compared to that of existing meta-heuristic algorithms. The proposed MBSGD-MMSNN model outperformed other state-of-the-art algorithms regarding the accuracy and learning performance. [ABSTRACT FROM AUTHOR]

With the support of enterprise intelligence system, an intelligent modeling method based on synthesizing various data sources and complex metabolic networks is adopted to provide technical support for the practical application of complex large-scale dynamic models. Academic research on international trade networks relies to a large extent on network models based on macro-national data and static analysis of international trade patterns, which are usually based on charts for trade flows and economic globalization. This paper presents an improved neural network algorithm for analyzing the complex dynamic meta-network model of international trade. By using a BP neural network to adjust the weighted values of the best neural network model, the need for continuous adjustment of neural network values is reduced, which may be an effective means to improve the efficiency of intrusion testing applications. The simulation results demonstrate that the model has good explanatory power. The globalization of enterprise markets, as the main driver of international business activities, has a significant impact on the establishment and development of national or regional international trade networks. Therefore, there is a need to study international trade networks using data at the micro level. This paper suggests an interdisciplinary approach to the study of international trade networks and a micro-level study of international trade networks. The model based on complex meta-network dynamic model elements, introduce the dynamic network representation, and use temporary labels to define the network boundary characteristics. [ABSTRACT FROM AUTHOR]

For a stable system frequency without any problems, the generation and demand equation must always be balanced. But the difficulty of precisely predicting the changes in the load is a big problem. Here comes the responsibility of the load frequency control (LFC) loop to maintain the equality between supply and demand. The LFC loop's performance depends on the control technique or controller used. In our work, we propose a new cascade controller PD-P-PID that is applied in LFC applications for the first time. To obtain the optimum settings of our cascade controller, we used an optimization method called the marine predators algorithm (MPA). To verify the robustness of our controller, it was tested on three different systems: a single-area multi-source power system, a two-area multi-source power system with AC–DC tie-lines, and a three-area hydro-thermal power system with the generation rate constraints and Governor dead band taken into account for a more practical study. The obtained results are compared with many published works based on objective function integral time absolute error, settling time Ts, and overshoot/undershoot of frequency and tie-line power deviation. MPA-tuned PID-PD controllers offer superior performance to other literature as it gives a dynamic response with a shorter settling time and fewer frequency and tie-line power deviation oscillations when step loads are perturbed. [ABSTRACT FROM AUTHOR]

In order to assure accurate modelling, this study presents a new technique for appropriately modelling and simulating a proton exchange membrane fuel cell (PEMFC) system. The PEMFC is a cleaner and more sustainable energy source as compared to fossil fuels. The fundamental idea is to minimize the sum of squared error (SSE) between the estimated and measured output voltage for the Ballard Mark V model in order to identify the model parameters of PEMFC stacks as efficiently as possible using a newly developed meta-heuristic called enhanced efficient optimization algorithm (EINFO). The proposed optimizer is considered an enhanced version of the original INFO algorithm. By balancing the exploration and exploitation phases better, the EINFO algorithm is intended to improve the performance of the original INFO approach and prevent local optima. The new method was tested on 23 benchmark functions and compared to the original INFO algorithm as well as other recently evolved optimizers. The algorithm is examined and compared with some literature meta-heuristics, including the particle swarm optimization, sine cosine algorithm, dragonfly algorithm, atom search optimization, Harris hawks optimization, and efficient optimization algorithm, using 50 independent runs, in terms of convergence speed and least SSE. When compared to other methods, the final findings show that, the suggested technique achieves the fastest convergence speed. [ABSTRACT FROM AUTHOR]

The feature selection (FS) is a binary optimization problem in the discrete optimization problem category. Maximizing the accuracy by using fewer features is the main aim of FS. Metaheuristic algorithms are widely used for FS in literature. Redundant and irrelevant features are selected/unselected by a binary metaheuristic optimization algorithm for FS. Search in a metaheuristic optimization algorithm is directed with a fitness function. The type and landscape of the search space affect the success of the algorithm. Generally, accuracy-based fitness functions of metaheuristic algorithms are used for FS. In this work, eleven existing and six novel fitness functions are analyzed on eleven various datasets with a novel binary threshold Lévy flight distribution (BTLFD) algorithm. The large datasets (Yale, ORL, and COIL20) have 1024 features. The medium datasets (SpectEW, BreastEW, Ionosphere, and SonarEW) has 22–60 features. The small datasets (Tic-tac-toe, WineEW, Zoo, and Lymphography) have 9–18 features. K-nearest neighbor is used as a classifier with five-fold cross-validation and the experimental results showed that three rarely used fitness functions produced more accurate solutions. In the comparisons, BTFLD outperformed 8 state-of-the-art metaheuristic algorithms on 21 datasets for FS. [ABSTRACT FROM AUTHOR]

Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared; whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI. [ABSTRACT FROM AUTHOR]

Automotive Open System Architecture (AUTOSAR), as an open, standardized framework for automotive electronic software development, has gradually become the standard followed by major automotive manufacturers and automotive electronic device suppliers. The electronic software system problem improves the development efficiency and portability of the system by reducing the development cost of automotive electronic software while ensuring the quality of products and services, which is beneficial for subsequent upgrades and updates of the system. In order to improve the reliability of the software component deployment algorithm based on AUTOSAR architecture, we proposed the TDCA algorithm. During the execution of the algorithm, communication volume and communication degree are introduced to improve the accuracy of the deployment plan by optimizing the bus load and ECU balancing. Algorithm comparison experiments show that comparing heuristic and linear optimization algorithms, the TDCA algorithm proposed in this paper has significant advantages in reducing bus load and ECU utilization. The algorithm can reduce the communication between cores and balance ECU load according to the constraints of AUTOSAR architecture. [ABSTRACT FROM AUTHOR]

This manuscript introduces a new socio-inspired metaheuristic technique referred to as leader–advocate–believer-based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behavior exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (leader, advocate, believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. The algorithm is validated using the CEC 2005 and CEC 2017 benchmark functions. The algorithm was applied to solve engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes and turning of titanium alloy in a minimum quantity lubrication environment. LAB algorithm was validated using the Friedman rank test. The results were compared with other algorithms such as FA, CI, GA, SA, PSO, Multi-CI, CMAES, ABC, SADE, CLPSO, BSA, IA, WOA, SHO, AVOA, LSHADE-Cn-EpsiN, FDB-SFS and LSHADE. For real-world problems, LAB outperformed SA, f best and f better by achieving 76%, 85% and 75% minimization of R a , respectively, for micro-milling with 0.7 mm tool diameter. For real-world problems, LAB achieved 81%, 72%, 85% minimization of R a when compared to SA, f best and f better for 1 mm tool diameter. LAB also achieved 24% and 34% minimization of B h and B t as compared to SA for micro-drilling with a tool diameter 0.5 mm. For tool diameters 0.8 mm and 0.9 mm, 16% and 3% minimization of B t , respectively, were achieved as compared to SA. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features and limitations of the algorithm are also discussed. [ABSTRACT FROM AUTHOR]

The maximal covering location problem refers to the problem of finding an optimal placement of given number of facilities to a network. The objective is to maximize the total demands of the covered population within some constraints. Several metaheuristic approaches were proposed to solve the problem as it is an NP hard problem. In this article, we have proposed a chemical reaction optimization (CRO)-based approach to solve MCLP. CRO is a metaheuristic based on population to solve optimization problems. We are proposing a method to solve MCLP by redesigning four fundamental operators of CRO. Sometimes the solutions get trapped into local maxima, so an additional repair operator is also designed to find optimal solutions. The proposed algorithm is tested for both small and large scales of instances of datasets, which include benchmark as well as random ones. The proposed method gives best percentage of coverage results in 91.60% of instances, and for the remaining 8.40% of instances it produces results with average error value 0.10% which is very close to the optimal value. Nevertheless, the proposed method performs very well in terms of computational time for all test instances (100%) on all datasets compared to state-of-the-art method (Atta_GA). Wilcoxon signed-rank test has been performed on the results of the proposed method to observe the statistical significance. For both real-world and random instances, the results of the statistical test are significant. [ABSTRACT FROM AUTHOR]

In this paper, a novel metaheuristic optimization algorithm, called chief executive officer election optimization algorithm (CEOA), is proposed, which is inspired by the process of electing a Chief Executive Officer (CEO) in a company. CEOA simulates three stages of electing a CEO, namely the mass-election stage, election stage and authorization stage. The list of candidates is confirmed during the mass-election stage. In the election stage, each candidate has its own public relation team, and all the employees are divided into three different groups. The loyalist group always supports the candidate of their own faction, which enhances the exploitation ability of the algorithm. The speculator group tries to seize the CEO's position directly by taking advantage of the current candidates through a variety of behaviors, which enhance the exploration ability of the algorithm. The neutralist group takes its own interests as the priority, supports the candidate who meet its own interests by comparing the candidate teams and improves the exploitation and exploration ability of the algorithm at the same time. The final CEO is confirmed in the authorization stage. In addition, a precocity judgment rule is introduced to ensure the effectiveness and rationality of the election process and improve the ability of the algorithm to escape from the local optimal region. The performance of CEOA is evaluated through twenty-one classical test functions, twenty-nine CEC2017 test functions, six real-world engineering optimization problems and three date clustering problems. The average value, standard deviation, Friedman mean rank and Wilcoxon signed rank are used as criteria. The above experiments are compared with the well-studied and recent optimizers, such as GA, PSO, DE, EO, MPA, AEFA, SHADE, ISOS, mSSA and PO. For the twenty-one classical test functions, in both cases of function shifting and without shifting, CEOA can gain the first rank, and its performance in high-dimensional search space also outperforms other algorithms. For the twenty-nine CEC2017 test functions, CEOA gain the third rank, only slightly behind SHADE and LSHADE-SPACMA. However, in the twenty-one classical test functions without shifting, Friedman mean rank of CEOA is 2.4762, while SHADE and LSHADE-SPACMA are only 4.5952 and 6.0476, respectively. The experimental results show that CEOA outperforms most of optimizers and is competitive compared with high-performance methods. In addition, CEOA ranks first on all six constrained engineering optimization problems and has good performance on three real-life datasets clustering problems, proving its applicability on real-world optimization problems. [ABSTRACT FROM AUTHOR]

In the past few decades, meta-heuristic algorithms have become a research hotspot in the field of evolutionary computing. The electric fish optimization algorithm (EFO) is a new meta-heuristic algorithm. Because of its simplicity and easy implementation, it has attracted the attention of researchers. However, it still faces premature convergence and poor balance between exploration and exploitation. To address this problems, an orthogonal electric fish optimization algorithm with quantization (QOXEFO) is proposed in this paper. In QOXEFO, orthogonal cross-design and quantification technique are employed to enhance the diversity of population and convergence precision of EFO. Secondly, the dynamic boundary mechanism is adopted to improve the convergence speed of EFO. At the same time, a sine-based update strategy of active electrolocation is used to change the direction of movement of individuals, thereby helping them jump out of the local optimum. Finally, the CEC2017 benchmark function and Speed reducer design problem are used to verify the performance of the proposed QOXEFO. Experimental results and statistical analysis show that compared with 9 famous evolutionary algorithms, QOXEFO is competitive in solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Diabetes is a set of long-term metabolic issues characterized by high blood glucose levels over a drawn-out time. It is a challenging and threatening disease because nearly half of patients who have diabetes do not know that they have it. Data mining techniques have been heavily utilized to predict diabetes in its earliest stage, and therefore can potentially reduce medication costs. Most of the existing research used metaheuristics to optimize neural network parameters or used other data mining algorithms to classify diabetes. This research integrates design of experiments (DOE), genetic algorithm (GA), and multilayer perceptron (MLP) to classify diabetic patients. Two sets of factorial designs were used to determine an optimal set of parameters for the GA. GA is used to optimize the parameters in the MLP, where the MLP acts as a simulator within the GA to provide the fitness evaluation of the solutions. The best resulting solution from DOE–GA–MLP represents the optimized MLP that achieves the highest classification accuracy in diabetes patients. The proposed approach outperforms eight different classification algorithms in terms of accuracy, sensitivity, and F1-score. The developed DOE–GA–MLP model presents a robust predictive tool that can help healthcare professionals identify patients with diabetes in its early stage. Intervention and/or preventive strategies can be implemented in those identified cases to improve patient care and possibly reduce diabetes rates and potential expenditures. [ABSTRACT FROM AUTHOR]

In this era of trying to get more information from data, demand forecasting plays a very important role for companies. Companies implement many of their plans based on these forecasts. However, forecasting demand is not always easy. Demand is divided into many different classes depending on its structure. One of them is the problem of intermittent demand. Estimating intermittent demand is a more difficult problem than estimating series with low variability and no null demand. When working with intermittent demands, either a good demand forecast or a good inventory policy is required. Determining an efficient inventory policy is important in terms of meeting customer demand and customer satisfaction. In this study, an inventory lower bound and upper bound are calculated to balance the inventory cost of intermittent demand and the lost sale cost. For this purpose, 7 different test data with intermittent demand structure were studied. A mathematical model is proposed to calculate the cost with the upper and lower inventories under intermittent demand. A feasible solution could not be obtained with the proposed model, and a fitness function for the relevant model was proposed. This function was run on test data using genetic algorithm (GA) and particle swarm optimization (PSO). The results and solution times of GA and PSO were compared. In this way, the variability of demand and the difference between arrival times of demands were met with minimal cost. [ABSTRACT FROM AUTHOR]

This study proposes a two-stage surrogate-assisted meta-heuristic algorithm named SDAMA-SPS to solve computationally expensive problems with high dimensions. In this algorithm, a surrogate-assisted monkey algorithm with dynamic adaptation (SDAMA) is presented to globally search for the best solution of the first stage, and a surrogate-based perturbation search (SPS) is designed to perform a more intensive local search for the final optimal solution. In the first stage, a global radial basis function (RBF) surrogate model is constructed with all historical solutions and is used to evaluate the positions of monkeys in the climb process and watch-jump process. Such global RBF model is updated with the positions of monkeys and their real objective function values after the second round of the climb process for each iteration. In the second stage, a local RBF surrogate model is built with a set of current best solutions, which can help to select the most promising sample so as to further locally improve the solution searched in the first stage. Experimental studies are conducted on eight benchmark optimization problems with the number of dimensions varying from 30 to 100, and numerical results show that the proposed algorithm achieves better performance than five other state-of-the-art surrogate-assisted algorithms with a limited budget of function evaluations. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the traditional parameter identification methods of photovoltaic cell model, including low accuracy, slow convergence speed, easy to be trapped in local optimum so on, a parameter identification method of photovoltaic cell model based on improved elephant herding optimization algorithm is proposed in this paper. The fast-moving operator is used, which greatly improves the convergence speed and global searching ability of the algorithm. The elitist strategy is also introduced where the worst individual is replaced with the optimal individual to improve the convergence speed and shorten the optimization time of the algorithm. When applied to the parameter identification of the photovoltaic cell model, the identification result of the improved elephant herding optimization algorithm proves to be better than other algorithms. The parameter identification of the photovoltaic cell model under different radiation conditions is carried out, and the fitting result of identification data is highly consistent with the measured data. Experiments validate that the improved elephant herding optimization algorithm can accurately and effectively identify the parameters of the photovoltaic cell model in different environments. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to sensors in different geographic locations. There are three critical issues:—what data should be replicated?—when should the data be replicated? and—where the new replicas should be placed? These three main open questions must be tackled for data replication in the cloud environments. This strategy, the identification and mode of the data replication problem are designed as a multi-objective optimization with modern meta-heuristic optimization method. Therefore, a new hybrid method using Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA) is proposed in this paper. Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing. Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic locations. The performance of the designed AOASSA strategy to tackling the data replication problem is evaluated using different datasets of different sizes. To validate the AOASSA strategy, a set of experiments was carried out to validate the proposed strategy AOASSA. Experiment results show the superiority of AOASSA over its competitors in terms of performance measures, such as least cost path, distance, and bandwidth. [ABSTRACT FROM AUTHOR]

This study proposes a new metaheuristic algorithm, which is called Artificial Locust Swarm Optimization (ALSO), inspired by random jumping and plant invasion behavior of locust swarms. Locusts interact in two different ways of searching for food: social and familial. In the familial phase, small locust groups search foods in a local area and the locusts share their information in the social phase. The proposed algorithm is less likely to trap into the local solution than other methods and has high performance in the sensitivity of the global solution. In addition, it is effective not only for the solution of black-box optimization problems but also for the solution of problems with an irregular objective function. The ALSO algorithm is compared with other recent and well-known optimization algorithms on 22 benchmark functions and 3 real engineering design problems. Simulation results prove that the ALSO algorithm is very competitive when compared to the other algorithms. Moreover, it even requires the less runtime and memory space under the same conditions. [ABSTRACT FROM AUTHOR]

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed. [ABSTRACT FROM AUTHOR]

We study a novel version of the capacitated facility location problem, which includes incompatibilities among customers. For this problem, we propose and compare on a fair common ground a portfolio of metaheuristic techniques developed independently from each other. We tested our techniques on a new dataset composed of instances of increasing size, varying from medium to very large ones. The outcome is that the technique based on data mining has been able to outperform the others in most instances, except for a few large cases, for which it is overcome by the simpler greedy one. In order to encourage future comparisons on this problem, we make the instances, solution validator and implementations of the metaheuristic techniques available to the community. [ABSTRACT FROM AUTHOR]

In this study, an entropy-based grey wolf optimizer (IEGWO) algorithm is proposed for solving global optimization problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between exploitation and exploration, and the premature convergence of grey wolf optimizer algorithm and consists of three aspects: Firstly, we proposed an information entropy-based population generation strategy to optimize the distribution of initial grey wolf pack. Secondly, a modified dynamic position update equation based on information entropy is introduced to maintain the population diversity in the process of iteration, thus avoiding premature convergence. Thirdly, a nonlinear convergence strategy is proposed to balance the exploration and exploitation. The performance of the proposed IEGWO algorithm is assessed on the CEC2014 and CEC2017 test suites and compared with other meta-heuristic algorithms. Furthermore, two engineering design problems and one real-world problem are also solved using the IEGWO algorithm. The experimental and statistical results indicate that the IEGWO algorithm has better solution accuracy and robustness than the compared algorithms in solving global optimization problems. [ABSTRACT FROM AUTHOR]

The primary aim of Virtual Machine Placement (VMP) is the mapping of Virtual Machines (VMs) to Physical Machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption significantly. The inefficient VMP leads to wastage of resources and excessive energy consumption and increases the overall operational cost of the data center. A Metaheuristic Virtual Machine Placement Framework towards the Power Efficiency of Sustainable Cloud Environment (MV-PESC) approach is suggested to address the issues mentioned above. An Extended Flower Pollination Optimization algorithm is suggested, which combines the concept of the Random Fit algorithm and the Flower Pollination Optimization algorithm. The proposed work's performance is evaluated using actual workload traces of the benchmark Google Cluster Data set. The obtained results are compared with various state-of-the-art and demonstrate a notable reduction in power consumption, the number of active PMs, and execution time up to 64.89%, 35%, and 21.12%, respectively. [ABSTRACT FROM AUTHOR]

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with well-known metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants. [ABSTRACT FROM AUTHOR]

Nowadays, all enterprises have adopted the means of informatization for enterprise management to adapt to the development of society. With the advent of the information age, enterprise transformation has become an inevitable trend. The management efficiency of enterprises is effectively improved, and the optimal scheduling and efficient management of personnel are realized to design and develop a human resource management (HRM) information system to meet the actual needs of enterprises. By studying the current problems of intelligent enterprise system and HRM information system, based on Java programming language, spring model–view–controller (MVC) web application system is combined with browser/server (B/S) framework, and construction is realized on an intelligent enterprise HRM information system. Aiming at the complexity of enterprise HRM, the meta-heuristic algorithm is adopted to optimize the human resource optimization scheduling module. Through the specific example data, the system implementation and model performance comparison experiments are carried out to further verify the effectiveness of the intelligent enterprise HRM information system proposed here. The results show that the HRM information system based on intelligent enterprise system realizes the effective collection and sorting of data, the running system meets the expected research objectives, and different modules can effectively perform specific functions; the algorithm based on meta-heuristic can realize the reasonable scheduling of personnel, and its model performance is significantly higher than the latest algorithm model. With the continuous increase in the number of events, what is improved is the optimal solution ability of the algorithm. Moreover, it decreases with the increase in the number of iterations, converging around 80 times, and the optimization efficiency reaches 86.35%; the system can find the optimal solution in a shorter time under the same number of iterations. Besides, after the system clustering, the accuracy of employee performance reaches 92%. The intellectualization of enterprise HRM greatly improves the office efficiency. [ABSTRACT FROM AUTHOR]

The concepts of generalized Hukuhara difference and interval differential equation play important role in the theory of interval uncertainty. These concepts have many applications in different branches of research, viz. optimization, information theory, inventory control and others. The goal of this work is to study an application of Hukuhara difference and interval differential equation in inventory management. In this paper, an inventory model for imperfect production process under warranty-dependent demand and carbon tax regulatory mechanism, is presented with the help of Hukuhara difference and interval differential equation. Also, using the interval arithmetic, the generalized Hukuhara difference, and the existence and uniqueness theorem of interval differential equation, the corresponding average profit function of this model is obtained. In order to maximize the average profit, a center-radius optimization technique is proposed. Some numerical examples are considered and solved by using different variant of quantum-behaved particle swarm optimization algorithms. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. [ABSTRACT FROM AUTHOR]

Hierarchical Single-Allocation Hub Median Facility Location Problem (SA-H-MP) is a well-known optimization problem having single objective function. The objective of SA-H-MP is to find a network of demand nodes, hubs and central hubs so that the service cost is minimized. In this article, we have proposed two meta-heuristic algorithms, Genetic Algorithm (GA) and its refined version and compare their performances with CPLEX (exact method), Simulated Annealing (SA) and Iterated Local Search (ILS). These approaches are validated on the well-known benchmark CAB data set. Proposed refined version of the GA is found to improve the result over other meta heuristics in more than 88% cases. [ABSTRACT FROM AUTHOR]

Archimedes optimization algorithm (AOA) is a new meta-heuristic algorithm which is based on Archimedes principle and mimics the buoyancy force received by an object in water. The AOA is designed according to physical principles and has been the object of many scholars' research because of its simple and reliable performance. In the course of the study, this paper finds that the AOA is flawed. In the iterative update of the algorithm, the buoyancy principle applied to the object is not completely followed. Through the investigation and analysis of this problem, it is found that the algorithm design which follows the buoyancy principle completely is more advantageously and persuasively, and named the corrected algorithm CAOA. The performance of the CAOA and other comparison optimization algorithms is tested in benchmark functions CEC2017 under equal conditions to verify the ideas proposed in this paper. In the solution accuracy with dimensions of 30 and 50, the comprehensive score of the CAOA is 31 and 33 and ranks first in all algorithms. In the statistical analysis, the CAOA compared with other algorithms one by one, and achieved the best results in all test functions. When compared with other algorithms, the CAOA ranked first. It is hoped that the verification of the ideas in this paper will help the AOA to develop better and optimize the development of the algorithm. [ABSTRACT FROM AUTHOR]