The unparalleled possibilities of Internet of Vehicles (IoV) development prompt the enhancement of in-vehicle medical emergency response. Nevertheless, the IoV environment is still affected by data privacy, latency, and network instability, which hamper effective and reliable emergency medical systems. In this regard, this article suggests the emergency-aware distributed edge intelligence (DEI) for medical response (EDEM) framework, a novel approach leveraging DEI to address these challenges. Specifically, EDEM introduces a hierarchical edge collaborative computing architecture that dynamically constructs learning domains based on a comprehensive medical data capability model. The framework incorporates an in-vehicle medical data reliability model and tailored latency and energy consumption models to optimize resource allocation and response times. Then, a deep-reinforcement-learning-based node selection algorithm ensures efficient task distribution across the network. Finally, EDEM’s dual-layer federated learning model features an emergency-aware adaptive aggregation mechanism and an adaptive medical model updating scheme for cross-domain scenarios, complemented by an emergency-weighted asynchronous model fusion approach. The superiority of EDEM over state-of-the-art methods is demonstrated through simulation results showing up to a 15% increase in model accuracy, a 30% reduction in response times, and a 20% better resource utilization efficiency. This implies that it can greatly enhance speed, accuracy, and reliability for in-vehicle emergency responses within IoV environments.

Due to its superiority in addressing label ambiguity, label distribution learning (LDL) has received wide attention from the community, such as image classification, emotion recognition, and big data processing. To efficiently process the data with label distribution, researchers have proposed to learn label-specific features (LSFs) that are the discriminative features for each class label. Although the LDL literature has seen many algorithms to learn LSFs, most of them ignore the characteristics of label distribution. Label distribution lies in real-value vector space with specific characteristics. In this article, we propose to learn label-distribution-specific features (LDSFs) for processing label distribution data by considering the structures of label distribution. We design a novel LDL method called LDL-LDSF to exploit LDSFs by considering the fuzzy cluster structures of label distribution data. First, LDL-LDSF learns LDSFs for the whole label distribution by jointly learning the label distribution and fuzzy C-means clustering. Second, it learns LDSFs for each label in a similar way. Third, it concatenates the learned LDSFs with the original features to deduce an LDL model. Finally, we conduct extensive experiments to justify that LDL-LDSF statistically outperforms several state-of-the-art LDL methods and validate the advantages of LDSFs for processing label distribution data.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

The rapid integration of cyber-physical systems (CPS) in urban transportation networks has revolutionized emergency medical services (EMS), enhancing response time and resource allocation. However, this interconnectedness exposes critical infrastructure to sophisticated cyber-attacks, potentially compromising patient safety and operational efficiency. The aim of this work is to develop a secure and efficient control method for EMS in transportation CPS (T-CPS) that can maintain optimal performance while defending against sophisticated, stealthy cyber-attacks. We propose a novel secure output-feedback control method for EMS (SOFC-EMS) in T-CPS that leverages the Kullback-Leibler divergence to characterize attack stealthiness and employs dynamic output-feedback control to maintain system stability and performance. Our approach utilizes ellipsoidal invariant reachable sets to analyze system behavior under various attack scenarios and optimizes controller parameters through convex optimization techniques. Simulation results show that the proposed SOFC-EMS method significantly reduces the reachable set volume, indicating improved system security. The method also performs better in practical EMS scenarios, reducing average ambulance response time and maintaining higher system safety scores under increasing attack frequencies. We demonstrate the method’s adaptability to different urban traffic patterns and attack intensities through consistent performance across various system parameters. While our simulations demonstrate promising results in a simplified urban grid, further research is needed to validate the method’s effectiveness in more complex, real-world urban environments.

Abstract With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy.

Feature selection (FS) is recognized for its role in enhancing the performance of learning algorithms, especially for high-dimensional datasets. In recent times, FS has been framed as a multi-objective optimization problem, leading to the application of various multi-objective evolutionary algorithms (MOEAs) to address it. However, the solution space expands exponentially with the dataset’s dimensionality. Simultaneously, the extensive search space often results in numerous local optimal solutions due to a large proportion of unrelated and redundant features [H. Adeli and H. S. Park, Fully automated design of super-high-rise building structures by a hybrid ai model on a massively parallel machine, AI Mag. 17 (1996) 87–93]. Consequently, existing MOEAs struggle with local optima stagnation, particularly in large-scale multi-objective FS problems (LSMOFSPs). Different LSMOFSPs generally exhibit unique characteristics, yet most existing MOEAs rely on a single candidate solution generation strategy (CSGS), which may be less efficient for diverse LSMOFSPs [H. S. Park and H. Adeli, Distributed neural dynamics algorithms for optimization of large steel structures, J. Struct. Eng. ASCE 123 (1997) 880–888; M. Aldwaik and H. Adeli, Advances in optimization of highrise building structures, Struct. Multidiscip. Optim. 50 (2014) 899–919; E. G. González, J. R. Villar, Q. Tan, J. Sedano and C. Chira, An efficient multi-robot path planning solution using a* and coevolutionary algorithms, Integr. Comput. Aided Eng. 30 (2022) 41–52]. Moreover, selecting an appropriate MOEA and determining its corresponding parameter values for a specified LSMOFSP is time-consuming. To address these challenges, a multi-objective self-adaptive particle swarm optimization (MOSaPSO) algorithm is proposed, combined with a rapid nondominated sorting approach. MOSaPSO employs a self-adaptive mechanism, along with five modified efficient CSGSs, to generate new solutions. Experiments were conducted on ten datasets, and the results demonstrate that the number of features is effectively reduced by MOSaPSO while lowering the classification error rate. Furthermore, superior performance is observed in comparison to its counterparts on both the training and test sets, with advantages becoming increasingly evident as the dimensionality increases.

Vehicular edge computing (VEC) systems face challenges in providing real-time intelligent transportation services due to limited computing resources at VEC servers, which lead to excessive delays or denial of services, especially for latency-critical tasks. This article proposes an augmented intelligence of things (AIoT) framework to enable priority-aware task offloading in VEC for vehicle road cooperation systems, maximizing overall system rewards under latency constraints. The framework incorporates an advanced dynamic resource management mechanism that adapts to real-time data and optimizes resource allocation using augmented intelligence models. The joint priority-aware application offloading and resource optimization problem is formulated as a constrained Markov decision process, and a deep Q-network (DQN)-based learning algorithm is employed to optimize the allocation of communication and computational resources based on application priorities and real-time channel/queue state information. Simulation results demonstrate that the proposed algorithm achieves significant improvements in weighted carrying capacity, high/low-priority task drop rates, and high/low-priority task queuing delays under varying overall task arrival rates, proportions of high/low-priority tasks, vehicle density, and task size compared to benchmark schemes. The proposed AIoT-enhanced DQN-based learning algorithm advances the field of VEC systems for vehicle road cooperation, offering practical advantages, such as increased efficiency, reduced latency, and improved resource utilization, ultimately enhancing user experience and enabling real-world applications in intelligent transportation systems.

Micro-expressions, fleeting facial movements lasting 1/25 to 1/3 of a second, offer crucial insights into genuine emotions, particularly valuable in online education settings. The rapid growth of English learning livestreams has heightened the need for accurate, real-time micro-expression recognition to enhance learner engagement and instructional effectiveness. However, existing methods need help with the subtle nature of these expressions, especially in dynamic, low-resolution streaming environments. This paper presents TSG–MER–ELL, a novel end-to-end network for micro-expression recognition in English learning livestreams, integrating temporal–spatial correlation and graph attention mechanisms. The framework addresses the unique challenges of real-time emotion analysis in online language education, where subtle facial cues are crucial in understanding learner engagement and comprehension. The temporal–spatial correlation module employs action units with spatio-temporal graph convolution to aggregate features from diverse facial regions, while transformer encoders construct long-range correlations. The graph attention module builds upon local facial areas to guide self-attention computations, yielding precise local correlation features. These global and local features are fused for the final micro-expression classification. We introduce an adaptive loss function that balances accuracy, efficiency, and relevance to linguistic context. Extensive experiments on SMIC, CASME II, and SAMM datasets, adapted for English learning scenarios, demonstrate TSG–MER–ELL’s superior performance over ten state-of-the-art baselines. The TSG–MER–ELL framework achieves top UF1 and UAR scores across all datasets, significantly improving recognition speed and accuracy. Ablation studies and visualizations of temporal–spatial features and graph attention weights provide insights into the framework’s effectiveness in capturing subtle emotional cues. TSG–MER–ELL’s robust performance in varied online learning conditions highlights its potential to enhance engagement, personalize instruction, and improve overall outcomes in virtual English language education.

The proliferation of multimedia-enabled IoT devices and edge computing enables a new class of data-intensive applications. However, analyzing the massive volumes of multimedia data presents significant privacy challenges. We propose a novel framework called generative adversarial privacy (GAP) that leverages generative adversarial networks (GANs) to synthesize privacy-preserving surrogate data for multimedia analytics across the IoT-Edge continuum. GAP carefully perturbs the GAN's training process to provide rigorous differential privacy guarantees without compromising utility. Moreover, we present optimization strategies, including dynamic privacy budget allocation, adaptive gradient clipping, and weight clustering to improve convergence and data quality under a constrained privacy budget. Theoretical analysis proves that GAP provides rigorous privacy protections while enabling high-fidelity analytics. Extensive experiments on real-world multimedia datasets demonstrate that GAP outperforms existing methods, producing high-quality synthetic data for privacy-preserving multimedia processing in diverse IoT-Edge applications.

The exponential growth of mobile data traffic in next generation networks has led to a significant increase in energy consumption, posing critical challenges for network operators. We propose DLLF-2EN, a novel energy-efficient framework that integrates deep learning-based load forecasting, an advanced power consumption model, and a comprehensive energy-saving strategy to address this issue. The load forecasting technique utilizes deep convolutional neural network and long short-term memory model, which is based on deep learning. This model is capable of capturing the spatiotemporal dependencies present in network traffic data. The power consumption model accurately characterizes the base stations’ static and dynamic power consumption components, facilitating the assessment of energy efficiency under various network scenarios. The energy-saving strategy combines base station sleep mode with discontinuous transmission and reception, as well as lightweight transmission of common signals, dynamically adapting the network operation based on the predicted traffic load. Furthermore, DLLF-2EN incorporates an intelligent power management system that leverages machine learning algorithms to continuously monitor the network, analyze collected data, and make optimal energy-saving decisions in real-time. Simulation demonstrate that the superior performance of DLLF-2EN in terms of load forecasting accuracy and energy efficiency compared to state-of-the-art baseline methods. The proposed framework represents a comprehensive solution for energy-efficient and sustainable next generation mobile networks, addressing the critical challenges of minimizing energy consumption while meeting the growing demands for high-quality mobile services.

Abstract Many classical methods have been used in automatic sleep stage classification but few methods explore deep learning. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming. In this paper, we propose an efficient convolutional neural network, Sle-CNN, for five-sleep-stage classification. We attach each kernel in the first layers with a trainable coefficient to enhance the learning ability and flexibility of the kernel. Then, we make full use of the genetic algorithm’s heuristic search and the advantage of no need for the gradient to search for the sleep stage classification architecture. We verify the convergence of Sle-CNN and compare the performance of traditional convolutional neural networks before and after using the trainable coefficient. Meanwhile, we compare the performance between the Sle-CNN generated through genetic algorithm and the traditional convolutional neural networks. The experiments demonstrate that the convergence of Sle-CNN is faster than the normal convolutional neural networks and the Sle-CNN generated by genetic algorithm outperforms the traditional handcrafted counterparts too. Our research suggests that deep learning has a great potential on electroencephalogram signal processing, especially with the intensification of neural architecture search. Meanwhile, neural architecture search can exert greater power in practical engineering applications. We conduct the Sle-CNN with the Python library, Pytorch, and the code and models will be publicly available.

With the emergence of deep neural networks, many research fields, such as image classification, object detection, speech recognition, natural language processing, machine translation, and automatic driving, have made major breakthroughs in technology and the research achievements have been successfully applied in many real-life applications. Combining evolutionary computation and neural architecture search (NAS) is an important approach to improve the performance of deep neural networks. Usually, the related researchers only focus on precision. Thus, the searched neural architectures always perform poorly in the other indexes such as time cost. In this article, a multi-objective evolutionary algorithm with a probability stack (MOEA-PS) is proposed for NAS, which considers the two objects of precision and time consumption. MOEA-PS uses an adjacency list to represent the internal structure of deep neural networks. Besides, a unique mechanism is introduced into the multi-objective genetic algorithm to guide the process of crossover and mutation when generating offspring. Furthermore, the structure blocks are stacked using a proxy model to generate deep neural networks. The results of the experiments on Cifar-10 and Cifar-100 demonstrate that the proposed algorithm has a similar error rate compared with the most advanced NAS algorithms, but the time cost is lower. Finally, the network structure searched on Cifar-10 is transferred directly to the ImageNet dataset, which can achieve 73.6% classification accuracy.

These days, hybrid nature-inspired population-based intelligent optimization methods are a wide range of the algorithms, which are used very often to solve real-world (industrial) optimizationproblems. As it was shown in [item 1) in the Appendix] by Slowik and Cpalka, nature-inspired methods can be divided into several groups of algorithms. In these groups of nature-inspired algorithms, we can find physics-based algorithms (gravitational search algorithm, harmony search algorithm, big bang-big crunch algorithm, etc.) and bio-inspired methods. In bio-inspired methods, we can find evolutionary algorithms (genetic algorithms, evolution strategies, genetic programming, etc.), swarm intelligence algorithms (particle swarm optimization algorithm, ant colony optimization algorithm, bat algorithm, etc.), immune algorithms (clonal selection algorithm, negative selection algorithm, etc.), and others (flower pollination algorithm, great Salomon run, Japanese tree frogs calling, etc.). These four selected groups of nature-inspired population-based optimization algorithm are commonly used in creating hybrid methods.

The article presents issues related to hybrid nature-inspired population-based algorithms of global optimization and their industrial applications. To this end, the article presents a general concept of nature-inspired population-based optimization methods and the ways in which those methods can be hybridized with other techniques. Concrete literature-based examples have been used to illustrate each type of hybridization. This article also demonstrates the publication popularity of selected hybrid nature-inspired population-based optimization algorithms and indicates their most common application areas. Moreover, this article refers to the computational and implementation complexity of the algorithms in question. Next, the focus shifts to industrial applications of hybrid nature-inspired population-based optimization methods. References have been made to numerous works that present different versions of hybrid optimization algorithms, showing the areas of their practical application. Moreover, this article discusses the problems inherent in hybrid optimization methods as well as indicates open research points in this field.

Abstract Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top-N recommendations are presented for the corresponding user according to the most feasible cluster’s members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other meta-heuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function.

Abstract In medical science, collecting and classifying data from various diseases is a vital task. The confused and large amounts of data are problems that prevent us from achieving acceptable results. One of the major problems for diabetic patients is a failure to properly diagnose the disease. As a result of this mistake in diagnosis or failure in early diagnosis, the patient may suffer from complications such as blindness, kidney failure, and cutting off the toes. Nowadays, doctors diagnose the disease by relying on their experience and knowledge and performing complex and time-consuming tests. One of the problems with current diabetic, diagnostic methods is the lack of appropriate features to diagnose the disease and consequently the weakness in its diagnosis, especially in its early stages. Since diabetes diagnosis relies on large amounts of data with many parameters, it is necessary to use machine learning methods such as support vector machine (SVM) to predict the complications of diabetes. One of the disadvantages of SVM is its parameter adjustment, which can be accomplished using metaheuristic algorithms such as particle swarm optimization algorithm (PSO), genetic algorithm, or grey wolf optimizer (GWO). In this paper, after preprocessing and preparing the dataset for data mining, we use SVM to predict complications of diabetes based on selected parameters of a patient acquired by laboratory test using improved GWO. We improve the selection process of GWO by employing dynamic adaptive middle filter, a nonlinear filter that assigns appropriate weight to each value based on the data value. Comparison of the final results of the proposed algorithm with classification methods such as a multilayer perceptron neural network, decision tree, simple Bayes, and temporal fuzzy min–max neural network (TFMM-PSO) shows the superiority of the proposed method over the comparable ones.

Abstract The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

Abstract The open shop scheduling problem involves a set of activities that should be run on a limited set of machines. The purpose of scheduling open shops problem is to provide a timetable for implementation of the entire operation so that the total execution time is reduced. The tasks scheduling problem in open shops is important in many applications due to the arbitrariness of the processing sequence of each job and lack of a prioritization of its operations. This is an NP-hard problem and obtaining an optimal solution to this problem requires a high time complexity. Therefore, heuristic techniques are used to solve these problems. In this paper, we investigate the tasks scheduling problem in open shops using the Bat Algorithm (BA) based on ColReuse and substitution meta-heuristic functions. The heuristic functions are designed to increase the rate of convergence to the optimal solution. To evaluate the performance of the proposed algorithm, standard open shop benchmarks were used. The results obtained in each benchmark are compared with those of the previous methods. Finally, after analyzing the results, it was found that the proposed BA had a better performance and was able to generate the best solution in all cases.

Abstract The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

Abstract Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal.

The papers in this special issue focus on nature-inspired optimization methods in fuzzy systems. These methods which are very often used to solve complex optimization problems that cannot be efficiently solved by traditional optimization algorithms. Optimization of fuzzy systems is also a complex optimization task involving continuous, integer, and combinatorial problems. For example, selection of input attributes of the fuzzy system, design of fuzzy system structure, selection of membership functions, and selection of inference operators can be seen as combinatorial optimization problems, whereas selection of the parameters in membership functions and fuzzy rules are continuous optimization problem. In addition, optimization of a fuzzy system becomes a multiobjective optimization problems when we take both interpretability and accuracy of the fuzzy systems into account. Thus, applications of nature-inspired optimization.

The solutions proposed in this article are based on our experience with fuzzy systems (FSs), their interpretability, and population-based algorithms (PBAs). They provide a consistent approach to the design of interpretable FSs. First, PBAs can make a useful tool for selecting both parameters and the FS structure. In practice, such structure is usually chosen by trial and error. Second, in this article, we propose a new multipopulation PBA, which uses a variety of search formulas. This helps to eliminate the problem of incorrect PBA selection. Third, we propose interesting solutions for the interpretability of FSs with trapezoidal membership functions. These functions are well suited for modeling ranges of linguistic variables. We are particularly interested in providing them with original interpretability criteria, which are used by a PBA to design FSs. Furthermore, we offer an original way of setting up trapezoidal functions, which prevents them from overlapping with each other. Fourth, we believe that interpretability can be achieved through a capable extension of fuzzy rules. That is the reason why we used weights and dedicated operators for their processing. In this article, a different extension of the rules base is proposed. It involves adding relation operators (ROs) (e.g., “>” and “>=”), which can be used to model linguistic phrases, e.g. “electrical voltage less than or equal to high.” An additional task of the PBA is the automatic selection of an RO, which facilitates the extraction of knowledge from the data. The approach proposed in this article was tested using well-known classification benchmarks.

This special section of the IEEE Transactions on Industrial Informatics is dedicated to 'Nature Inspired Methods in Industry Applications.' This special section is focused on the development, adaptation, and application of the methods inspired by nature in real-life engineering applications. A total of 127 papers were submitted to our special section. Based on the reviewers' comments, eleven papers have been accepted for publication, while seven papers are still under review (as of January 2, 2018). These eleven accepted papers have been grouped into four thematic sections as follows: 1) Evolutionary algorithms (EA), 2) Swarm intelligence algorithms (SIA), 3) Hybridization of EA with SIA, and 4) Other nature-inspired methods. Section II of this Introduction presents the accepted papers related to the EA research topic. In Section III, the accepted papers related to SIA research topic are discussed. The accepted papers related to the research topic based on the hybridization of EA with SIA are briefly described in Section IV. In Section V, we present the accepted papers related to the research topic based on some other nature-inspired methods. In Section VI, the summary is given.

In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.