Search

This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

It also reports simulation-based comparison study with other optimization algorithms such as particle swarm optimization, differential evolution, and whale optimization algorithm. The experiment results of the algorithm in the form of precision are compared with conventional algorithms to draw conclusions about the efficacy of the proposed algorithm. In the paper titled "Analysis of Amplitude Scintillation and Positioning Error of IRNSS/GPS/SBAS Receiver for Heavy Rainy Days", the authors examine the characteristics of amplitude scintillation on an IRNSS/SBAS/GPS receiver with all seven satellites of the Indian Regional Navigation Satellite System (IRNSS) at the low-altitude station IITRAM, Ahmedabad, on days with heavy rain. In the paper "Model Order Reduction Based Power System Stabilizer Design Using Improved Whale Optimization Algorithm", the authors present an optimization technique I viz i . improved whale optimization for designing a power system stabilizer using model-order reduction for a modified single machine infinite bus system. [Extracted from the article]

Abstract: The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm. [Copyright &y& Elsevier]

The current paper presents a soft-sensor method based on belief-rule-base (BRB) system for solving the problem of tipping paper permeability measurement in the tobacco industry. Firstly, BRB is utilized to establish a model between the feature variables in the tipping paper image and the corresponding paper permeability obtained by the traditional measuring device. Unlike the traditional case of BRB, this paper adds the output attribute as the optimization parameters. In this way, the feasible solution space can be enlarged to obtain an effective BRB model. Second, in order to find the reasonable parameters of BRB in a complex nonconvex solution space, an enhanced differential evolutionary (DE) algorithm is developed to train BRB, which not only embeds a simplex method to stress the balance between the global and local search but also designs a perturbation operation and an adaptively selected mutation strategy to maintain the diversity of search direction. The test results and comparisons based on the data collected from a cigarette factory in China show that the presented method is effective and robust. [ABSTRACT FROM AUTHOR]

Applied computing and artificial intelligence methods have been attracting growing interest in recent years due to their effectiveness in solving technical problems. Hao et al. [[14]] present an unsupervised fault diagnosis methodology to leverage the generated MPCs of different working conditions to diagnose the actual unlabeled MPCs. The paper by Ainapure et al. [[17]] proposes a new cross-domain fault diagnosis method with enhanced robustness. The paper by Saeed et al. [[1]] proposes an approach to building an AutoML data-dependent CNN model (DeepPCANet) customized for DR screening automatically. [Extracted from the article]

In the process of promoting energy green transformation, the optimization of regional integrated energy system faces many challenges such as cooperative management, energy saving and emission reduction, as well as uncertainty of new energy output. Therefore, this paper proposes a multi-agent game operation method of regional integrated energy system based on carbon emission flow. First, this paper establishes a carbon emission flow calculation model for each subject, and proposes a comprehensive tariff model based on the carbon emission flow, which discounts the carbon emissions from the power supply side to the power consumption side. Secondly, considering the interests of each subject, this paper establishes the decision- making model of each subject. And the new energy uncertainty, the cost of energy preference of prosumers, and the thermal inertia of buildings are considered in the decision model. Finally, the model is solved using differential evolution algorithm and solver. The case study verifies that the comprehensive electricity pricing model based on carbon emission flow developed in this paper can play a role in balancing economy and low carbon. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

We present a metapopulation dynamic model for the diffusively-coupled rock-paper-scissors (RPS) game with mutation in scale-free hierarchical networks. We investigate how the RPS game changes by mutation in scale-free networks. Only the mutation from rock to scissors (R-to-S) occurs with rate μ. In the network, a node represents a patch where the RPS game is performed. RPS individuals migrate among nodes by diffusion. The dynamics are represented by the reaction-diffusion equations with the recursion formula. We study where and how species coexist or go extinct in the scale-free network. We numerically obtained the solutions for the metapopulation dynamics and derived the transition points. The results show that, with increasing mutation rate μ , the extinction of P species occurs and then the extinction of R species occurs, and finally only S species survives. Thus, the first and second dynamical phase transitions occur in the scale-free hierarchical network. We also show that the scaling law holds for the population dynamics which suggests that the transition points approach zero in the limit of infinite size. [ABSTRACT FROM AUTHOR]

In the third paper, I Deep transfer learning: a novel glucose prediction framework for new subjects with Type 2 diabetes i , the authors designed a novel cross-subject glucose prediction framework by integrating instance-based and network-based deep transfer learning via segmented continuous glucose monitoring time series. Control and optimization Six papers have devoted to the computational intelligence-based decision-making and analysis of complex systems. Complex systems, which are composed of many interconnected and interactive functional parts, widely exist in the nature and human society. [Extracted from the article]

Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated. [ABSTRACT FROM AUTHOR]

The real world data are complex in nature and addition to that a large number of features add more value to the complexity. However, the features associated with the data may be redundant and erroneous in nature. To deal with such type of features, feature selection plays a vital role in computational learning. The reduction in the dimensionality of the dataset not only reduces the computational time required for classification but also enhances the classification accuracy by removing the misleading features. This paper presents a Filter Approach using Elitism based Multi-objective Differential Evolution algorithm for feature selection (FAEMODE) and the novelty lies in the objective formulation, where both linear and nonlinear dependency among features have been considered to handle the redundant and unwanted features of a dataset. Finally, the selected feature subsets of 23 benchmark datasets are tested using 10-fold cross validation with four well-known classifiers to endorse the result. A comparative analysis of the proposed approach with seven filter approaches and two conventional as well as three metaheuristic based wrapper approaches have been carried out for validation. The result reveals that the proposed approach can be considered as a powerful filter method for feature selection in various fields. [ABSTRACT FROM AUTHOR]

This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance. [ABSTRACT FROM AUTHOR]

The increased application of Information and Communications Technology in automation and digitization is the driving concept towards the actualization of the Industrial Revolution 4.0. Many aspects of industrial design and operations including offshore engineering have been automated. However, the selection procedure of mooring design parameters including azimuth angle, pretension, diameter, fairlead slope, and mooring radius, which is a critical aspect of mooring system design has remained heavily based on a manual approach despite the availability of sophisticated hydrodynamic analysis software. Up to date, there is no proper review on optimisation techniques that have the potential of automating these procedures. In the past, some of the few available mooring optimisation procedures consider only the mooring lines for the prediction of optimal platform offset without due consideration to the integrity of the risers. Thus, the significance of adopting an integrated riser-mooring design methodology in the optimisation of mooring design variables is discussed in this paper where the integrity of the riser is adequately represented using a Safe Operation (SAFOP) zone polar diagram. We also review the developmental transition from the use of classical to Evolutionary Algorithms (EA) in the optimisation procedure. EA has been acknowledged as a practical alternative for solving mooring optimisation problems. Hence, some of the EA techniques are presented and their computational capabilities explored. In the last part, a concise review of the application of optimisation techniques for mooring optimisation of the different offshore floating platforms is presented based on their capabilities in minimizing platform offset and efficiency in terms of simulation time. So far, EA techniques like the Differential Evolution (DE), variants of Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO) have been used to optimize mooring line design variables. Based on the discussions, the potentials of DE, RegPSO and other variants of PSO can be further explored for better efficiency. • Previous studies addressing differential evolution (DE), genetic algorithm (GE) and particle swarm optimisation (PSO) techniques used for optimisation of mooring parameters have been critically reviewed. • The integrated riser -mooring design methodology is effective in accounting for integrity of riser and mooring systems. • The application of evolutionary algorithms, particularly the variants and hybrids of DE ,PSO and GA, have proven to be efficient optimisation of mooring systems. • Some variants of PSO like the RegPSO have demonstrated improved capability in terms of escaping premature convergence , thereby resulting in better computational efficiency. [ABSTRACT FROM AUTHOR]

A multi-modal search space can be defined as having multiple attraction basins - each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers. [ABSTRACT FROM AUTHOR]

Logistics refers to the process of moving goods or materials from the point of origin to the point of destination, with the utilization of various resources including personnel, vehicles, facilities and equipment. In the paper entitled "Robust multi-product inventory optimization under support vector clustering-based data-driven demand uncertainty set", a robust multi-product inventory optimization approach is developed with an uncertainty set constructed from the available data using support vector clustering. Product sales forecasting would directly impact inventory management and customer service level and thus plays an important role in logistics systems. [Extracted from the article]

The Industrial Internet is based on the network, the platform is the core, and the security is the guarantee. The Industrial Internet connects all industry elements and the entire industry chain through the large-scale network infrastructure, collects and analyzes industry data in real time, and forms a new application model for a new generation of information communication. With the rapid development of industrial Internet technology, the scale of industrial Internet data will become larger and larger, and the data dimension will become higher and higher. How to efficiently use cluster analysis for industrial Internet big data mining is an urgent problem that needs to be solved. This paper proposes an improved differential evolution particle swarm algorithm for industrial Internet big data clustering analysis. Differential Evolution (DE) strategy can improve the problem that the particle swarm optimization (PSO) algorithm tends to fall into local optimum in the later stage as the number of iterations increases. Considering the influence of the randomness of the arrangement order of the cluster center vectors among the individuals on the learning and updating among individuals, this paper designs a method of adaptively adjusting the arrangement order of the cluster center vectors to optimize the cluster center vector with maximum similarity among individuals. In order to effectively evaluate our method, both industrial and non-industrial datasets are selected. The experimental results verify the feasibility and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue on the application of machine learning in geotechnical engineering. The issue includes 19 articles that explore different applications of machine learning in this field, such as determining geotechnical parameters, predicting geotechnical disasters, and optimizing construction processes. The articles cover various topics in geotechnical engineering, including underground and foundation engineering, and discuss the use of machine learning algorithms to predict and estimate various parameters and behaviors. While machine learning shows potential in improving predictions, the papers also acknowledge the limitations of purely data-driven models and the need to incorporate mechanical models and improve data collection methods. Overall, these articles provide valuable insights and serve as a starting point for future research in the field. [Extracted from the article]

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Simulation results on the modified IEEE 73-bus test system including wind farms generation prove the efficiency of the proposed algorithm as the impacts of energy storage system modules on the grid-scale system flexibility, investment plans, and power system economics. TOPIC B: SHORT-TERM GENERATION/LOAD FORECASTING OF IES Ge et al., in their paper "Short-Term Load Forecasting of Integrated Energy System Considering the Peak-Valley of Load Correlations" propose an integrated energy system (IES) short-term load forecasting method based on load-correlation peaks and valleys. The increasing necessity of tearing down the barriers between different energy-related disciplines and developing effective means to coordinate and integrate various energy systems has given rise to the concept of integrated energy system (IES), which have received great attention from multiple technical communities in recent years. TOPIC E: STATE ESTIMATION AND MODEL IDENTIFICATION/CALIBRATION OF IES Xu et al., in their paper "A Real-Time State Estimation Framework for Integrated Energy System Considering Measurement Delay" propose real-time state estimation framework for the gas-electricity coupled system. [Extracted from the article]

As an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) have been widely used. However, traditional CNNs have limited feature representation capabilities, thereby making it challenging to determine their hyperparameters. This paper proposes a fault diagnosis method that combines a 1D-CNN with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of CNN models, improves the diagnostic accuracy, and enhances the representation of fault feature information in CNNs. First, the 1D-CNN is improved by combining it with an attention mechanism to enhance the fault feature information. Second, a swarm intelligence algorithm based on Differential Evolution (DE) and Grey Wolf Optimization (GWO) is proposed, which not only improves the convergence accuracy, but also increases the search efficiency. Finally, the improved 1D-CNN alongside hyperparameters optimization are used to diagnose the faults of rolling bearings. By using the Case Western Reserve University (CWRU) and Jiangnan University (JNU) datasets, when compared to other common diagnosis models, the results demonstrate the usefulness and dependability of the DE-GWO-CNN algorithm in fault diagnosis applications by demonstrating the increased diagnostic accuracy and superior anti-noise capabilities of the proposed method. The fault diagnosis methodology presented in this paper can accurately identify faults and provide dependable fault classification, thereby assisting technicians in promptly resolving faults and minimizing equipment failures and operational instabilities. [ABSTRACT FROM AUTHOR]

Currently, digitalization and digital transformation are actively expanding into various areas of human activity, and researchers are identifying urgent problems and offering new solutions regarding digital environments in industry [[1]], economics [[3]], medicine [[5]], ecology [[7]], education [[9]], etc. Conclusions The purpose of this Special Issue was to attract high-quality new papers in the field of applied and computational mathematics for digital environments, offering original solutions to various problems that are relevant and in demand in various fields of human activity. 10.3390/math10224297 21 Demidova L.A. A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature. I hope that these selected research papers are recognized as important and meaningful by the international scientific community and can form the basis for further research in the field of applied and computational mathematics for digital environments, solving complex problems in various disciplines and application areas. [Extracted from the article]

Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is an effective way to overcome this challenge. However, how to obtain high-quality samples for training the surrogate model and which method should be used to develop the surrogate model with higher accuracy remain important questions to explore. To this end, this paper innovatively adopted the quasi-Monte Carlo (QMC) method to sample from the prior space of unknown variables. Then, this paper established a variety of individual machine learning surrogate models, respectively, and screened three with higher training accuracy among them as the base-learning models (BLMs). The Stacking ensemble framework was utilized to integrate the three BLMs to establish the ensemble surrogate model for the groundwater LNAPL multiphase flow numerical simulation model. Finally, a hypothetical case of groundwater LNAPL contamination was designed. After evaluating the accuracy of the Stacking ensemble surrogate model, the differential evolution Markov chain (DE-MC) algorithm was applied to jointly identify information on groundwater LNAPL contamination source and key hydrogeological parameters. The results of this study demonstrated the following: (1) Employing the QMC method to sample from the prior space resulted in more uniformly distributed and representative samples, which improved the quality of the training data. (2) The developed Stacking ensemble surrogate model had a higher accuracy than any individual surrogate model, with an average R2 of 0.995, and reduced the computational burden by 99.56% compared to the inversion process based on the simulation model. (3) The application of the DE-MC algorithm effectively solved the GLCSI problem, and the mean relative error of the identification results of unknown variables was less than 5%. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method. [ABSTRACT FROM AUTHOR]