Applied Metaheuristic Computing

This paper presents a comparison of different metaheuristic approaches applied to the pilot sequence allocation problem in Massive Multiple-Input Multiple-Output (MIMO) systems. A modified version of the Genetic Algorithm (GA) as well as different versions of the Particle Swarm Optimization (PSO) Algorithm are used to maximize the system spectral efficiency under an inter-cell interference regime. The metaheuristic parameters were optimized and computational simulations under different scenarios parameters were conducted to verify the system performance impact in terms of system spectral efficiency, minimum and maximum spectral efficiency per user and the cumulative distribution function (CDF) of the users spectral efficiencies. The main contributions of this work are: the creation of a public available dataset; heuristic parameters tuning; findings related to the impact of sub-optimal pilot sequence allocation to the users in terms of maximal and minimal achievable user spectral efficiency and the robustness of some algorithms in scenarios with different system loadings. Full article

In this paper, an assembly sequence planning system, based on artificial neural networks, is developed. The problem of artificial neural network itself is largely related to symmetry at every stage of its creation. A new modeling scheme, known as artificial neural networks, takes into account selected DFA (Design for Assembly) rating factors, which allow the evaluation of assembly sequences, what are the input data to the network learning and then estimate the assembly time. The input to the assembly neural network procedure is the sequences for assembling the parts, extended by the assembly’s connection graph that represents the parts and relations between these parts. The operation of a neural network is to predict the assembly time based on the training dataset and indicate it as an output value. The network inputs are data based on selected DFA factors influencing the assembly time. The proposed neural network model outperforms the available assembly sequence planning model in predicting the optimum assembly time for the mechanical parts. In the neural networks, the BFGS (the Broyden–Fletcher–Goldfarb–Shanno algorithm), steepest descent and gradient scaling algorithms are used. The network efficiency was checked from a set of 20,000 test networks with randomly selected parameters: activation functions (linear, logistic, tanh, exponential and sine), the number of hidden neurons, percentage set of training and test dataset. The novelty of the article is therefore the use of parts of the DFA methodology and the neural network to estimate assembly time, under specific production conditions. This approach allows, according to the authors, to estimate which mechanical assembly sequence is the most advantageous, because the simulation results suggest that the neural predictor can be used as a predictor for an assembly sequence planning system. Full article

The aim of this work is to study the influence of lighting on different types of filters in order to create adaptive systems of perception in the visible spectrum. This problem is solved by estimating symmetry operations (operations responsible for image/image transformations). Namely, the authors are interested in an objective assessment of the possibility of reproducing the image of the object (objective symmetry of filters) after the application of filters. This paper investigates and shows the results of the most common edge detection filters depending on the light level; that is, the behavior of the system in a room with indirect natural and standard (according to the requirements of the educational process in Ukraine) electric lighting was studied. The methods of Sobel, Sobel x, Sobel y, Prewitt, Prewitt x, Prewitt y, and Canny were used and compared in experiments. The conclusions provide a subjective assessment of the performance of each of the filters in certain conditions. Dependencies are defined that allow giving priority to certain filters (from those studied) depending on the lighting. Full article

To improve the recognition accuracy of coal gangue images with the back propagation (BP) neural network, a coal gangue image recognition method based on BP neural network and ASGS-CWOA (ASGS-CWOA-BP) was proposed, which makes two key contributions. Firstly, a new feature extraction method for the unique features of coal and gangue images is proposed, known as “Encircle–City Feature”. Additionally, a method that applied ASGS-CWOA to optimize the parameters of the BP neural network was introduced to address to the issue of its low accuracy in coal gangue image recognition, and a BP neural network with a simple structure and reduced computational consumption was designed. The experimental results showed that the proposed method outperformed the other six comparison methods, with recognition of 95.47% and 94.37% in the training set and the test set, respectively, showing good symmetry. Full article

Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. The contribution of this work is two-fold: first, to describe an approach that comprehensively addresses the main activities carried out by practitioners during portfolio management (price forecasting, stock selection and portfolio optimization) and, second, to consider uptrends and downtrends in prices. Both aspects are relevant for practitioners but, to the best of our knowledge, the literature does not have an approach addressing them together. We propose to do it by exploiting various computational intelligence techniques. The assessment of the proposal shows that further improvements to the procedure are obtained when considering downtrends and that the procedure allows obtaining portfolios with better returns than those produced by the considered benchmarks. These results indicate that practitioners should consider the proposed procedure as a complement to their current methodologies in managing stock portfolios. Full article

This study aims to symmetrically improve the economy and environmental protection of combined cooling, heating and power microgrid. Hence, the characteristics of configuration ways of energy storage devices in traditional combined cooling, heating and power systems are analyzed, and a scheme for the operator to establish an energy storage station is designed. An improved aquila optimizer for the optimal configuration of the system is proposed to symmetrically enhance the economic and environmental protection performance. The feasibility of the proposed scheme is verified through experiments in three different places. The results show that the economic cost and exhaust emission of the system with energy storage station are reduced to varying degrees compared with the system with energy storage equipment alone and the system without energy storage equipment based on symmetry concept. Especially in Place 1, the scheme with energy storage station in the system can reduce the electric energy purchased from power grid by 43.29% and 61.09%, respectively, compared with other schemes. This study is conducive to promoting the development of clean energy, alleviating the energy crisis, reducing the power supply pressure of power grid, and improving the profits of operators by symmetrically considering the economic and environmental performance of the system. Full article

Clustering is a popular data analysis and data mining problem. Symmetry can be considered as a pre-attentive feature, which can improve shapes and objects, as well as reconstruction and recognition. The symmetry-based clustering methods search for clusters that are symmetric with respect to their centers. Furthermore, the K-means (K-M) algorithm can be considered as one of the most common clustering methods. It can be operated more quickly in most conditions, as it is easily implemented. However, it is sensitively initialized and it can be easily trapped in local targets. The Tabu Search (TS) algorithm is a stochastic global optimization technique, while Adaptive Search Memory (ASM) is an important component of TS. ASM is a combination of different memory structures that save statistics about search space and gives TS needed heuristic data to explore search space economically. Thus, a new meta-heuristics algorithm called (MHTSASM) is proposed in this paper for data clustering, which is based on TS and K-M. It uses TS to make economic exploration for data with the help of ASM. It starts with a random initial solution. It obtains neighbors of the current solution called trial solutions and updates memory elements for each iteration. The intensification and diversification strategies are used to enhance the search process. The proposed MHTSASM algorithm performance is compared with multiple clustering techniques based on both optimization and meta-heuristics. The experimental results indicate the superiority of the MHTSASM algorithm compared with other multiple clustering algorithms. Full article

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability. Full article

In this paper, we present a denoising network composed of a kernel prediction network and a deep generative adversarial network to construct an end-to-end overall network structure. The network structure consists of three parts: the Kernel Prediction Network (KPN), the Deep Generation Adversarial Network (DGAN), and the image reconstruction model. The kernel prediction network model takes the auxiliary feature information image as the input, passes through the source information encoder, the feature information encoder, and the kernel predictor, and finally generates a prediction kernel for each pixel. The generated adversarial network model is divided into two parts: the generator model and the multiscale discriminator model. The generator model takes the noisy Monte Carlo-rendered image as the input, passes through the symmetric encoder–decoder structure and the residual block structure, and finally outputs the rendered image with preliminary denoising. Then, the prediction kernel and the preliminarily denoised rendered image is sent to the image reconstruction model for reconstruction, and the prediction kernel is applied to the preliminarily denoised rendered image to obtain a preliminarily reconstructed result image. To further improve the quality of the result and to be more robust, the initially reconstructed rendered image undergoes four iterations of filtering for further denoising. Finally, after four iterations of the image reconstruction model, the final denoised image is presented as the output. This denoised image is applied to the loss function. We compared the results from our approach with state-of-the-art results by using the structural similarity index (SSIM) values and peak signal-to-noise ratio (PSNR) values, and we reported a better performance. Full article

In the past, because computer programs were restricted to perform only simple functions, the dependence on software was not large, resulting in relatively small losses after a failure. However, with the development of the software market, the dependence on software has increased considerably, and software failures can cause significant social and economic losses. Software reliability studies were previously conducted under the assumption that software failures occur independently. However, as software systems become more complex and extremely large, software failures are becoming frequently interdependent. Therefore, in this study, a software reliability model is developed under the assumption that software failures occur in a dependent manner. We derive the software reliability model through the number of software failure and fault detection rate assuming point symmetry. The proposed model proves good performance compared with 21 previously developed software reliability models using three datasets and 11 criteria. In addition, to find the optimal release time, a cost model using the developed software reliability model was presented. To determine this release time, four parameters constituting the software reliability model were changed by 10%. By comparing the change in the cost model and the optimal release time, it was found that parameter $b$ had the greatest influence. Full article

Digital evidence is critical in cybercrime investigations because it is used to connect individuals to illegal activity. Digital evidence is complicated, diffuse, volatile, and easily altered, and as such, it must be protected. The Chain of Custody (CoC) is a critical component of the digital evidence procedure. The aim of the CoC is to demonstrate that the evidence has not been tampered with at any point throughout the investigation. Because the uncertainty associated with digital evidence is not being assessed at the moment, it is impossible to determine the trustworthiness of CoC. As scientists, forensic examiners have a responsibility to reverse this tendency and officially confront the uncertainty inherent in any evidence upon which they base their judgments. To address these issues, this article proposes a new paradigm for ensuring the integrity of digital evidence (CoC documents). The new paradigm employs fuzzy hash within blockchain data structure to handle uncertainty introduced by error-prone tools when dealing with CoC documents. Traditional hashing techniques are designed to be sensitive to small input modifications and can only determine if the inputs are exactly the same or not. By comparing the similarity of two images, fuzzy hash functions can determine how different they are. With the symmetry idea at its core, the suggested framework effectively deals with random parameter probabilities, as shown in the development of the fuzzy hash segmentation function. We provide a case study for image forensics to illustrate the usefulness of this framework in introducing forensic preparedness to computer systems and enabling a more effective digital investigation procedure. Full article

Information hiding can be seen everywhere in our daily life, and this technology improves the security of information. The requirements for information security are becoming higher and higher. The coverless information hiding with the help of mapping relationship has high capacity, but there is still a problem in which the secret message cannot find the mapping relationship and the process requires extra storage burden during the transmission. Therefore, on the basis of symmetric reversible watermarking, the paper introduces the two-level mechanism and novel arrangements to solve the problem of sufficient diversity of features and has better capacity and image quality as a whole. Besides, for the security of secret message, this paper designs a new encryption model based on Logistic mapping. This method only employs coverless information hiding of one carrier image to transmit secret message with the help of the two-level mechanism and look-up table. Reversible information hiding is applied to embed the generated location table on the original image so that ensures storage and security. The experiment certifies that the diversity of hash code is increased by using the two-level image mechanism and the quality of the image is excellent, which proves the advantages of the proposed symmetric method over the previous algorithm. Full article

This paper investigates the Discrete Targets Search Problem, (DTSP), which aims to quickly search for discrete objects scattered in a vast symmetry region. Different from continuous function extremal value search, the discrete points search cannot make use of the properties of regular functions, such as function analytic, single/multiple extreme, and monotonicity. Thus, in this paper a new search scheme based on Lévy random distribution is investigated. In comparison with the $Traditional$$Carpet$ search or $Random$ search based on other distributions, DTSP can provide much faster search speed which is demonstrated by simulation with different scales problems for the selected scenarios. The simulations experiment proves that DTSP is faster for searching for a discrete single target or multiple targets in a wide area. It provides a new method for solving the discrete target search problem. Full article

Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues. Full article

Identifying the positive definiteness of even-order real symmetric tensors is an important component in tensor analysis. $\mathcal{H}$-tensors have been utilized in identifying the positive definiteness of this kind of tensor. Some new practical criteria for identifying $\mathcal{H}$-tensors are given in the literature. As an application, several sufficient conditions of the positive definiteness for an even-order real symmetric tensor were obtained. Numerical examples are given to illustrate the effectiveness of the proposed method. Full article

The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability. Full article

When adopting cloud computing, cybersecurity needs to be applied to detect and protect against malicious intruders to improve the organization’s capability against cyberattacks. Having network intrusion detection with zero false alarm is a challenge. This is due to the asymmetry between informative features and irrelevant and redundant features of the dataset. In this work, a novel machine learning based hybrid intrusion detection system is proposed. It combined support vector machine (SVM) and genetic algorithm (GA) methodologies with an innovative fitness function developed to evaluate system accuracy. This system was examined using the CICIDS2017 dataset, which contains normal and most up-to-date common attacks. Both algorithms, GA and SVM, were executed in parallel to achieve two optimal objectives simultaneously: obtaining the best subset of features with maximum accuracy. In this scenario, an SVM was employed using different values of hyperparameters of the kernel function, gamma, and degree. The results were benchmarked with KDD CUP 99 and NSL-KDD. The results showed that the proposed model remarkably outperformed these benchmarks by up to 5.74%. This system will be effective in cloud computing, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion. Full article

In a round-trip carsharing system, stations must be located in such a way that allow for maximum user coverage with the least walking distance as well as offer certain degrees of flexibility for returning. Therefore, a balance must be stricken between these factors. Providing a satisfactory system can be translated into an optimization problem and belongs to an NP-hard class. In this article, a novel optimization model for the round-trip carsharing fleet placement problem, called Fleet Placement Problem (FPP), is proposed. The optimization in this work is multiobjective and its NP-hard nature is proven. Three different optimization algorithms: PolySCIP (exact method), heuristics, and NSGA-II (metaheuristic) are investigated. This work adopts three real instances for the study, instead of their abstracts where they are most commonly used. They are two instance:, in the city of Luxembourg (smaller and larger) and a much larger instance in the city of Munich. Results from each algorithm are validated and compared with solution from human experts. Superiority of the proposed FPP model over the traditional methods is also demonstrated. Full article

Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimization (ACO). In order to increase both the quality and the stability of image edge detection, a vector called pheromone sensitivity level, PSL, was used within ACO. Each ant in the algorithm has one assigned element from PSL, representing the ant’s sensibility to the artificial pheromone. A matrix of artificial pheromone with the edge information of the image is built during the process. Demi-contractions in terms of the mathematical admissible perturbation are also used in order to obtain feasible results. In order to enhance the edge results, post-processing with the DeNoise convolutional neural network (DnCNN) was performed. When compared with Canny edge detection and similar techniques, the sensitive ACO model was found to obtain overall better results for the tested medical images; it outperformed the Canny edge detector by 37.76%. Full article

With the frequent occurrence of network security events, the intrusion detection system will generate alarm and log records when monitoring the network environment in which a large number of log and alarm records are redundant, which brings great burden to the server storage and security personnel. How to reduce the redundant alarm records in network intrusion detection has always been the focus of researchers. In this paper, we propose a method using the whale optimization algorithm to deal with massive redundant alarms. Based on the alarm hierarchical clustering, we integrate the whale optimization algorithm into the process of generating alarm hierarchical clustering and optimizing the cluster center and put forward two versions of local hierarchical clustering and global hierarchical clustering, respectively. To verify the feasibility of the algorithm, we conducted experiments on the UNSW-NB15 data set; compared with the previous alarm clustering algorithms, the alarm clustering algorithm based on the whale optimization algorithm can generate higher quality clustering in a shorter time. The results show that the proposed algorithm can effectively reduce redundant alarms and reduce the load of IDS and staff. Full article

The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 network models were made using various training methods, including the steepest descent method, the conjugate gradients method, and Broyden–Fletcher–Goldfarb–Shanno algorithm. Changes to network parameters also included the following activation functions: linear, logistic, tanh, exponential, and sine. The simulation results suggest that the neural predictor would be used as a predictor for the assembly sequence planning system. This paper discusses a new modeling scheme known as artificial neural networks, taking into account selected criteria for the evaluation of assembly sequences based on data that can be automatically downloaded from CAx systems. Full article

Mobile Computing (MC) is a relatively new concept in the world of distributed computing that is rapidly gaining traction. Due to the dynamic nature of mobility and the limited bandwidth available on wireless networks, this new computing environment for mobile devices presents significant challenges in terms of fault-tolerant system development. As a consequence, traditional fault-tolerance techniques are inherently inapplicable to these systems. External circumstances often expose mobile systems to failures in communication or data storage. In this article, a quantum game theory-based recovery model is proposed in the case of a mobile host’s failure. Several of the state-of-the-art recovery protocols are selected and analyzed in order to identify the most important variables influencing the recovery mechanism, such as the number of processes, the time needed to send messages, and the number of messages logged-in time. Quantum game theory is then adapted to select the optimal recovery method for the given environment variables using the proposed utility matrix of three players. Game theory is the study of mathematical models of situations in which intelligent rational decision-makers face conflicting interests (alternative recovery procedures). The purpose of this study is to present an adaptive algorithm based on quantum game theory for selecting the most efficient context-aware computing recovery procedure. The transition from a classical to a quantum domain is accomplished in the proposed model by treating strategies as a Hilbert space rather than a discrete set and then allowing for the existence of linear superpositions between classical strategies; this naturally increases the number of possible strategic choices available to each player from a numerable to a continuous set. Numerical data are provided to demonstrate feasibility. Full article

The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/s. Full article

A novel computational method to evaluate the Sommerfeld integral (SI) efficiently and accurately is presented. The method rewrites the SI into two parts, applying discrete complex image method (DCIM) to evaluate the infinite integral while using double exponential quadrature rules (DE rules) for the computation of the finite part. Estimation of signal parameters via rotational invariance techniques (ESPRIT) is used to improve the accuracy and efficiency of extracting DCIM compared to the generalized pencil of function (GPOF). Due to the symmetry of the horizontal layered media, the Green function, representing the seismic fields due to a point source, can be written in the form of Sommerfeld integral in cylindrical coordinate system and be calculated by the proposed method. The performance of the method is then compared to the DE rules with weighted average partition extrapolation (WA), which shows a good agreement, with computational time reduced by about 40%. Full article

A new sigma identification protocol (SIP) based on matrix power function (MPF) defined over the modified medial platform semigroup and power near-semiring is proposed. It is proved that MPF SIP is resistant against direct and eavesdropping attacks. Our security proof relies on the assumption that MPF defined in the paper is a candidate for one-way function (OWF). Therefore, the corresponding MPF problem is reckoned to be a difficult one. This conjecture is based on the results demonstrated in our previous studies, where a certain kind of MPF problem was proven to be NP-complete. Full article

Data sharing is a central aspect of judicial systems. The openly accessible documents can make the judiciary system more transparent. On the other hand, the published legal documents can contain much sensitive information about the involved persons or companies. For this reason, the anonymization of these documents is obligatory to prevent privacy breaches. General Data Protection Regulation (GDPR) and other modern privacy-protecting regulations have strict definitions of private data containing direct and indirect identifiers. In legal documents, there is a wide range of attributes regarding the involved parties. Moreover, legal documents can contain additional information about the relations between the involved parties and rare events. Hence, the personal data can be represented by a sparse matrix of these attributes. The application of Named Entity Recognition methods is essential for a fair anonymization process but is not enough. Machine learning-based methods should be used together with anonymization models, such as differential privacy, to reduce re-identification risk. On the other hand, the information content (utility) of the text should be preserved. This paper aims to summarize and highlight the open and symmetrical problems from the fields of structured and unstructured text anonymization. The possible methods for anonymizing legal documents discussed and illustrated by case studies from the Hungarian legal practice. Full article

Optimization techniques keep road performance at a good level using a cost-effective maintenance strategy. Thus, the trade-off between cost and road performance is a multi-objective function. This paper offers a new multi-objective stochastic algorithm for discrete variables, which is called the integer search algorithm (ISA). This algorithm is applied to an optimal pavement maintenance management system (PMMS), where the variables are discrete. The PMMS optimization can be achieved by maximizing the condition of pavement with a minimum cost at specified constraints, so the PMMS is a constrained multi-objective problem. The ISA and genetic algorithm (GA) are applied to improve the performance condition rating (PCR) of the pavement in developing countries, where the annual budget is limited, so a minimum cost for three years’ maintenance is scheduled. Study results revealed that the ISA produced an optimal solution for multi-function objectives better than the optimal solution of GA. Full article

In the last decade, the devices and appliances utilizing the Internet of Things (IoT) have expanded tremendously, which has led to revolutionary developments in the network industry. Smart homes and cities, wearable devices, traffic monitoring, health systems, and energy savings are typical IoT applications. The diversity in IoT standards, protocols, and computational resources makes them vulnerable to security attackers. Botnets are challenging security threats in IoT devices that cause severe Distributed Denial of Service (DDoS) attacks. Intrusion detection systems (IDS) are necessary for safeguarding Internet-connected frameworks and enhancing insufficient traditional security countermeasures, including authentication and encryption techniques. This paper proposes a wrapper feature selection model (SSA–ALO) by hybridizing the salp swarm algorithm (SSA) and ant lion optimization (ALO). The new model can be integrated with IDS components to handle the high-dimensional space problem and detect IoT attacks with superior efficiency. The experiments were performed using the N-BaIoT benchmark dataset, which was downloaded from the UCI repository. This dataset consists of nine datasets that represent real IoT traffic. The experimental results reveal the outperformance of SSA–ALO compared to existing related approaches using the following evaluation measures: TPR (true positive rate), FPR (false positive rate), G-mean, processing time, and convergence curves. Therefore, the proposed SSA–ALO model can serve IoT applications by detecting intrusions with high true positive rates that reach 99.9% and with a minimal delay even in imbalanced intrusion families. Full article

Several trusted tasks use consensus algorithms to solve agreement challenges. Usually, consensus agreements are used to ensure data integrity and reliability in untrusted environments. In many distributed networking fields, the Proof of Work (PoW) consensus algorithm is commonly used. However, the standard PoW mechanism has two main limitations, where the first is the high power consumption and the second is the 51% attack vulnerability. In this paper, we look to improve the PoW consensus protocol by introducing several proof rounds. Any given consensus node should resolve the game of the current round $Roun{d}_i$ before participating in the next round $Roun{d}_{i+1}$. Any node that resolves the game of $Roun{d}_i$ can only pass to the next round if a predetermined number of solutions has been found by other nodes. The obtained evaluation results of this technique show significant improvements in terms of energy consumption and robustness against the 51% and Sybil attacks. By fixing the number of processes, we obtained an energy gain rate of 15.63% with five rounds and a gain rate of 19.91% with ten rounds. Full article

Distributed denial of service (DDoS) attacks often use botnets to generate a high volume of packets and adopt controlled zombies for flooding a victim’s network over the Internet. Analysing the multiple sources of DDoS attacks typically involves reconstructing attack paths between the victim and attackers by using Internet protocol traceback (IPTBK) schemes. In general, traditional route-searching algorithms, such as particle swarm optimisation (PSO), have a high convergence speed for IPTBK, but easily fall into the local optima. This paper proposes an IPTBK analysis scheme for multimodal optimisation problems by applying a revised locust swarm optimisation (LSO) algorithm to the reconstructed attack path in order to identify the most probable attack paths. For evaluating the effectiveness of the DDoS control centres, networks with a topology size of 32 and 64 nodes were simulated using the ns-3 tool. The average accuracy of the LS-PSO algorithm reached 97.06 for the effects of dynamic traffic in two experimental networks (number of nodes = 32 and 64). Compared with traditional PSO algorithms, the revised LSO algorithm exhibited a superior searching performance in multimodal optimisation problems and increased the accuracy in traceability analysis for IPTBK problems. Full article

This paper introduces a novel methodology for the optimum design of linear tuned mass dampers (TMDs) to improve the seismic safety of structures through a novel Whale Optimization Algorithm (WOA). The algorithm is aimed to reduce the maximum horizontal peak displacement of the structure, and the root mean square (RMS) response of displacements as well. Furthermore, four additional objective functions, derived from multiple weighted linear combinations of the two previously mentioned parameters, are also studied in order to obtain the most efficient TMD design configuration. The differential evolution method (DEM), whose effectiveness has been previously demonstrated for TMD applications, and an exhaustive search (ES) process, with precision to two decimal positions, are used to compare and validate the results computed through WOA. Then, the proposed methodology is applied to a 32-story case-study derived from an actual building, and multiple ground acceleration time histories are considered to assess its seismic performance in the linear-elastic range. The numerical results show that the proposed methodology based on WOA is effective in finding the optimal TMD design configuration under earthquake loads. Finally, practical design recommendations are provided for TMDs, and the robustness of the optimization is demonstrated. Full article

Design: At the heart of time series forecasting, if nonlinear and nonstationary data are analyzed using traditional time series, the results will be biased. At the same time, if just using machine learning without any consideration given to input from traditional time series, not much information can be obtained from the results because the machine learning model is a black box. Purpose: In order to better study time series forecasting, we extend the combination of traditional time series and machine learning and propose a hybrid cascade neural network considering a metaheuristic optimization genetic algorithm in space–time forecasting. Finding: To further show the utility of the cascade neural network genetic algorithm, we use various scenarios for training and testing while also extending simulations by considering the activation functions SoftMax, radbas, logsig, and tribas on space–time forecasting of pollution data. During the simulation, we perform numerical metric evaluations using the root-mean-square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) to demonstrate that our models provide high accuracy and speed up time-lapse computing. Full article

Multi-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. Multi-class classification is more complex than binary classification. In binary classification, only the decision boundaries of one class are to be known, whereas in multiclass classification, several boundaries are involved. The objective of this investigation is to propose a metaheuristic, optimized, multi-level classification learning system for forecasting in civil and construction engineering. The proposed system integrates the firefly algorithm (FA), metaheuristic intelligence, decomposition approaches, the one-against-one (OAO) method, and the least squares support vector machine (LSSVM). The enhanced FA automatically fine-tunes the hyperparameters of the LSSVM to construct an optimized LSSVM classification model. Ten benchmark functions are used to evaluate the performance of the enhanced optimization algorithm. Two binary-class datasets related to geotechnical engineering, concerning seismic bumps and soil liquefaction, are then used to clarify the application of the proposed system to binary problems. Further, this investigation uses multi-class cases in civil engineering and construction management to verify the effectiveness of the model in the diagnosis of faults in steel plates, quality of water in a reservoir, and determining urban land cover. The results reveal that the system predicts faults in steel plates with an accuracy of 91.085%, the quality of water in a reservoir with an accuracy of 93.650%, and urban land cover with an accuracy of 87.274%. To demonstrate the effectiveness of the proposed system, its predictive accuracy is compared with that of a non-optimized baseline model, single multi-class classification algorithms (sequential minimal optimization (SMO), the Multiclass Classifier, the Naïve Bayes, the library support vector machine (LibSVM) and logistic regression) and prior studies. The analytical results show that the proposed system is promising project analytics software to help decision makers solve multi-level classification problems in engineering applications. Full article