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Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Toxicology.)

The main objectives of this study were to 1) review the literature on the applications of soft computing concepts to the field of human factors and ergonomics (HFE) between 2013 and 2022 and 2) highlight future developments and trends. Multiple soft computing methods and techniques have been investigated for their ability to address various applications in HFE effectively. These techniques include fuzzy logic, artificial neural networks, genetic algorithms, and their combinations. Applications of these methods in HFE have been highlighted in one hundred and four articles selected from 406 papers. The results of this study help address the challenges of complexity, vagueness, and imprecision in human factors and ergonomics research through the application of soft computing methodologies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions., Objective: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions., Methods: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts., Results: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper., Conclusions: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness., (© 2023. The Author(s).)

Introduction: Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability., Objectives: The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters., Methods: The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials., Results: Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme., Conclusion: Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Production and hosting by Elsevier B.V.)

The harm of agricultural pests presents a remarkable effect on the quality and safety of edible farm products and the monitoring and identification of agricultural pests based on the Internet of Things (IoT) produce a large amount of data to be transmitted. To achieve efficient and real-time transmission of the sensors' data for pest monitoring, this paper selects 235 geographic coordinates of agricultural pest monitoring points and uses genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) to optimize the data transmission paths of sensors. The three intelligent algorithms are simulated by MATLAB software. The results show that the optimized path based on PSO can make the shortest time used for transmitting data, and its corresponding minimum time is 4.868012 s. This study can provide a reference for improving the transmission efficiency of agricultural pest monitoring data, provide a guarantee for developing real-time and effective pest control strategies, and further reduce the threat of pest damage to the safety of farm products.

We introduce a regret-based fitness assignment strategy for evolutionary algorithms to find Nash equilibria in noncooperative simultaneous combinatorial game theory problems where it is computationally intractable to enumerate all decision options of the players involved in the game. Applications of evolutionary algorithms to non-cooperative simultaneous games have been limited due to challenges in guiding the evolutionary search toward equilibria, which are usually inferior points in the objective space. We propose a regret-based approach to select candidate decision options of the players for the next generation in a multipopulation genetic algorithm called Regret-Based Nash Equilibrium Sorting Genetic Algorithm (RNESGA). We show that RNESGA can converge to multiple Nash equilibria in a single run using two- and three- player competitive knapsack games and other games from the literature. We also show that pure payoff-based fitness assignment strategies perform poorly in three-player games., (© 2022 Massachusetts Institute of Technology.)

Environmental variability often degrades the performance of algorithms designed to capture the global convergence of a given search space. Several approaches have been developed to challenge environmental uncertainty by incorporating biologically inspired notions, focusing on crossover, mutation, and selection. This study proposes a bio-inspired approach called NEAT-HD, which focuses on parent selection based on genetic similarity. The originality of the proposed approach rests on its use of a sigmoid function to accelerate species formation and contribute to population diversity. Experiments on two classic control tasks were performed to demonstrate the performance of the proposed method. The results show that NEAT-HD can dynamically adapt to its environment by forming hybrid individuals originating from genetically distinct parents. Additionally, an increase in diversity within the population was observed due to the formation of hybrids and novel individuals, which has never been observed before. Comparing two tasks, the characteristics of NEAT-HD were improved by appropriately setting the algorithm to include the distribution of genetic distance within the population. Our key finding is the inherent potential of newly formed individuals for robustness against dynamic environments., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The use of multidimensional forced-choice questionnaires has been proposed as a means of improving validity in the assessment of non-cognitive attributes in high-stakes scenarios. However, the reduced precision of trait estimates in this questionnaire format is an important drawback. Accordingly, this article presents an optimization procedure for assembling pairwise forced-choice questionnaires while maximizing posterior marginal reliabilities. This procedure is performed through the adaptation of a known genetic algorithm (GA) for combinatorial problems. In a simulation study, the efficiency of the proposed procedure was compared with a quasi-brute-force (BF) search. For this purpose, five-dimensional item pools were simulated to emulate the real problem of generating a forced-choice personality questionnaire under the five-factor model. Three factors were manipulated: (1) the length of the questionnaire, (2) the relative item pool size with respect to the questionnaire's length, and (3) the true correlations between traits. The recovery of the person parameters for each assembled questionnaire was evaluated through the squared correlation between estimated and true parameters, the root mean square error between the estimated and true parameters, the average difference between the estimated and true inter-trait correlations, and the average standard error for each trait level. The proposed GA offered more accurate trait estimates than the BF search within a reasonable computation time in every simulation condition. Such improvements were especially important when measuring correlated traits and when the relative item pool sizes were higher. A user-friendly online implementation of the algorithm was made available to the users., (© 2021. The Author(s).)

Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of reflecting elements at the IRS and its passive operation represent an important challenge in the acquisition of the instantaneous channel state information (I-CSI) of all links as it adds a very high overhead to the system and requires equipping the IRS with radio-frequency chains. To overcome this problem, a new approach is proposed in order to optimize beamforming at the BS and the phase shifts at the IRS without considering any knowledge of I-CSI but while only exploring the statistical channel state information (S-CSI). We aim at maximizing the user-achievable rate subject to a maximum transmit power constraint. To achieve this goal, we propose a new two-phase framework. In the first phase, both the beamforming at the BS and IRS are designed based only on S-CSI and, in the second phase, the previously designed beamforming pair is used as an initial solution, and beamforming at the BS and IRS is designed only by considering the feedback of the SNR at UE. Moreover, for each phase, we propose new methods based on Genetic Algorithms. Results show that the developed algorithms can approach beamforming with I-CSI but with significantly reduced channel estimation overhead.

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.

Cuckoo search (CS) algorithm is a classical swarm intelligence algorithm widely used in a variety of engineering optimization problems. However, its search accuracy and convergence speed still have a lot of room for improvement. In this paper, an improved version of the CS algorithm based on intelligent perception strategy, adaptive invasive weed optimization (AIWO), and elite cross strategy, called IIC-CS is proposed. Firstly, the intelligent perception strategy can update the value according to the searching state. Moreover, the CS is hybridized with the AIWO to improve the searching performance of the algorithm. Additionally, the elite cross strategy is employed to enhance the exploration capability and exploitation capability of the algorithm. Combining the improvements of these three methods, the performance of the CS algorithm is significantly improved. Meanwhile, 23 classical benchmark functions, some CEC2014 and CEC2018 benchmark functions are used to test the search accuracy and convergence rate of the IIC-CS. Furthermore, some classical or state-of-the-art algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO), bat algorithm (BA), ant lion optimizer (ALO) and cuckoo search (CS) algorithm, invasive weed optimization (IWO), integrated cuckoo search optimizer (ICSO) and improved island cuckoo search (iCSPM2) are used to make comparisons. Through the statistical results of the experiments, we find that the IIC-CS algorithm can achieve better results on most benchmark functions compared to other algorithms, thus demonstrating the effectiveness of the improvements and the superiority of the IIC-CS algorithm. [ABSTRACT FROM AUTHOR]

In this research, the optimization of the geometric parameters of a 3-layer composite plate with an elliptical cutout in the center was investigated utilizing an artificial neural network and genetic algorithm for the first time to obtain the minimum ratio of linear buckling load to weight and the ratio of the diameters of the ellipse. After completing 294 simulations in Abaqus finite element software and forming an artificial neural network with two hidden layers, the function obtained in the artificial neural network was used as the objective function in the genetic algorithm, and the optimal values for the angle of the ellipse with the horizon axis, the diameter ratio of ellipses as well as the fiber angle of the middle layer of the composite were obtained. using artificial neural networks, three algorithms, Levenberg-Marquardt, Bayesian regularization; and scaled conjugate gradient backpropagation, were compared and the number of neurons in each algorithm was compared. The results showed that Levenberg-Marquardt's algorithm is more accurate compared to other algorithms. In the end, the optimal values for the angle of the ellipse with the horizontal axis; as the fiber angle of the middle layer of the composite and, the ratio of the oval diameters as α = 12.3001 degree , θ = 5.9481 degree and D = 1.5690. [ABSTRACT FROM AUTHOR]

To accelerate the convergence rate of high-dimensional optimization problems, inspired by the cooperative hunting process of spider colonies named Anelosimus eximius, a new A. eximius colony algorithm (AECA) was proposed to solve combinatorial optimization problems. In the AECA, a certain direction component of the problem solution is represented as a certain direction in which a spider travels, so that a high-dimensional optimization problem can be transformed into multiple low-dimensional optimization problems. The AECA includes two intelligent behaviors: the random walk of spiders and the summoning of the initiator. The random walk of spiders ensures the diversity of spider colonies, whereas the summoning of the initiator can accelerate the convergence rate. We theoretically proved that the AECA is globally convergent. The inversion method of asteroid spectrum reflectance template can be used to solve the problem that the measured planetary spectrum is affected by the asteroid absorption effect and improves the accuracy of celestial Doppler difference velocimetry, which uses celestial spectrum to provide the information of velocity measurement for navigation. The essence of this method is the optimal combination problem of intrinsic mode functions (IMFs). We applied the AECA to the inversion of the planetary spectrum reflectance template. Experimental results show that, compared with genetic algorithms (GAs), the AECA can obtain the optimal combination of spectrum reflectance templates faster. In addition, to verify the universality of the AECA, for the classical knapsack problem, the AECA has a better optimization effect, faster convergence rate, and higher stability than other swarm intelligence algorithms such as GA, discrete particle swarm optimization, and the quantum genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for the design of marine metal sandwich plates to enhance the anti-blast performance of ship structures. The proposed method combines a neural network with the sparrow search algorithm to efficiently optimize the structure. The orthogonal design method is utilized to select appropriate sample points for the surrogate model. Dynamic responses of the ship under blast loading are obtained using the ABAQUS finite element software. By establishing an anti-blast surrogate model, the optimization problem is simplified. To overcome the limitations of the traditional backpropagation neural network, genetic algorithm and Adam algorithm are employed. Finally, the optimal solution is obtained based on the sparrow search algorithm. The results enable us to determine the structural dimensions with the best blast resistance. The proposed optimisation method can provide inspiration for improving the safety of ships. [ABSTRACT FROM AUTHOR]

Hellenic Open University has been developing Onlabs, a virtual biology laboratory simulating its on-site laboratory, for its students to be trained before the on-site learning activities. The evaluation of user performance in Onlabs is based on a scoring algorithm, which admits some optimization by means of Genetic Algorithms and Artificial Neural Networks. Moreover, for a particular experimental procedure (microscoping), we have experimented with incorporating into it some background knowledge about the procedure, which allows one to break it down in a series of conceptually linked steps in a hierarchical fashion. In this work, we review the flat and hierarchical modes used for the calibration of the automated assessment mechanism and offer an experimental comparison of both approaches with the aim of devising automated scoring schemes which are fit for training in an at-a-distance learning context. Overall, the genetic algorithm fails to deliver good convergence results in the non-hierarchical setting but performs better in the hierarchical one. On the other hand, the neural network most of the time converges, with the non-hierarchical network achieving a slightly better convergence than the hierarchical one, with the latter, however, delivering a smoother and more realistic assessment mechanism. [ABSTRACT FROM AUTHOR]

With the advent of low-power ultra-fast hardware and GPUs, virtual reality (VR) has gained a lot of prominence in the past few years and is being used in various areas, such as education, entertainment, scientific visualization, and computer-aided design. VR-based applications are highly interactive, and one of the most important performance metrics for these applications is the motion-to-photon-delay (MPD). MPD is the delay from the user's head movement to the time at which the image gets updated on the VR screen. Since the human visual system can even detect an error of a few pixels (very spatially sensitive), the MPD should be as small as possible. Popular VR vendors use the GPU-accelerated Asynchronous Time Warp (ATW) algorithm to reduce the MPD. ATW reduces the MPD if and only if the warping operation finishes just before the display refreshes. However, due to the competition between the different constituent applications for the single, shared GPU, the GPU-accelerated ATW algorithm suffers from an unpredictable ATW latency, making it challenging to find the ideal time instance for starting the time warp and ensuring that it completes with the least amount of lag relative to the screen refresh. Hence, the state-of-the-art is to use a separate hardware unit for the time-warping operation. Our approach, PredATW, uses an ML-based hardware predictor to predict the ATW latency for a VR application, and then schedule it as late as possible while running the time-warping operation on the GPU itself. As far as we know, this is the first work to do so. Our predictor achieves an error of only 0.22 ms across several popular VR applications for predicting the ATW latency. As compared to the baseline architecture, we reduce deadline misses by 80.6%. [ABSTRACT FROM AUTHOR]

In order to implement personalized recommendation algorithms for e-commerce, the author proposes a genetic fuzzy algorithm based on user behavior to improve the sales, personalized recommendation, user satisfaction, and purchase matching performance of e-commerce. Collect data based on e-commerce personalized preference recommendation information, extract the associated feature quantities of personalized data for clustering processing, and then combine fuzzy B-means clustering method to achieve e-commerce personalized recommendation. According to the individual preferences of e-commerce, the collected data samples are fitted with differences and restructured, and a genetic evolution method is adopted for global optimization. The experimental results show that the optimized genetic fuzzy algorithm used in this method has improved stability and accuracy compared to the PSO method, with an accuracy increase of 4%. This proves that the algorithm can provide the services needed by users more quickly and is an effective means. [ABSTRACT FROM AUTHOR]

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well‐known single‐objective algorithms and state‐of‐the‐art multiobjective evolutionary algorithm—Nondominated Sorting Genetic Algorithm (NSGA‐II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

This study presents a new approach for Maximum Power Point Tracking (MPPT) by combining the honey badger algorithm (HBA) with a Genetic Algorithm (GA). The integration aims to optimize photovoltaic (PV) system performance in partial shading conditions (PSCs). Initially, the HBA is utilized to explore extensively and identify potential solutions while avoiding local optima. If necessary, the GA is then employed to escape local optima through selection, crossover, and mutation operations. On average, this proposed method has a 40% improvement in tracking time and 0.77% in efficiency compared with the HBA. In a dynamic case, the proposed method achieves a 4.81% improvement compared to HBA. [ABSTRACT FROM AUTHOR]

The coverage tasks of mobile robots are evolving towards large-scale and intelligent di- rections demanding urgent requirements for the coverage efficiency and environmental adaptabil- ity of coverage path planning. To address the inadequate adaptability of the traditional boustro- phedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decom- position method. a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establis- hing the mapping relationship between the starting and ending positions of cells, a genetic algo- rithm was employed to optimize the selection of cells' starting positions and the global access or- der. The research results show that: 1) when processing maps with dimensions of 1 300 pixels. the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area: 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total op- eration time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99,91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

The question "How does it work" has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process. [ABSTRACT FROM AUTHOR]

In this article, an improved harmony search algorithm (IHSA) that utilizes opposition-based learning is presented for solving the maximal covering location problem (MCLP). The MCLP is a well-known facility location problem where a fixed number of facilities are opened at a given potential set of facility locations such that the sum of the demands of customers covered by the open facilities is maximized. Here, the performance of the harmony search algorithm (HSA) is improved by incorporating opposition-based learning that utilizes opposite, quasi-opposite and quasi-reflected numbers. Moreover, a local search heuristic is used to improve the performance of the HSA further. The proposed IHSA is employed to solve 83 real-world MCLP instances. The performance of the IHSA is compared with a Lagrangean/surrogate relaxation-based heuristic, a customized genetic algorithm with local refinement, and an improved chemical reaction optimization-based algorithm. The proposed IHSA is found to perform well in solving the MCLP instances. [ABSTRACT FROM AUTHOR]

The School Bus Routing Problem (SBRP) is a challenging optimization problem that has received increasing attention in recent years. The problem is composed of three sub-problems: facility location selection, assignment problem, and vehicle routing problem, which can be solved in a single stage or across multiple stages. In this study, we propose a novel two-stage approach to solve the SBRP that combines Fuzzy C Means (FCM) and K-means clustering algorithms with a Genetic Algorithm (GA). In the first stage, we used FCM and K-means to identify the optimal bus stop locations and assigned students to the nearest stop based on the distance metric. This two-stage approach reduces the search space and improves the efficiency of the GA in the second stage. In the second stage, we employed the GA to generate the optimal vehicle route that minimizes the total distance traveled by all vehicles. We compared our results with those in the literature and found that the K Means-GA approach outperformed the previous results. However, the FCM-GA approach yielded significantly inferior results, indicating that the choice of clustering algorithm plays a crucial role in the performance of the overall system. Our study provides insights into the importance of selecting appropriate clustering algorithms for solving the SBRP and proposes a two-stage solution that can be easily implemented in real-world scenarios. Our approach reduces the computational time and provides an effective solution for reducing the total distance traveled by school buses. [ABSTRACT FROM AUTHOR]

The fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit. [ABSTRACT FROM AUTHOR]

This study introduces an integrated power quality (PQ) conditioner, referred to as UPQC, that is linked with photovoltaic (PV) and battery energy systems (BSS) in order to address and solve PQ issues. It is proposed to employ the Levenberg–Marquardt (LM) backpropagation (LMBP) trained artificial neural network control (ANNC) technique for generating reference signal for converters in UPQC. This approach eliminates the need for traditional abc to dq0 to αβ conversions. Additionally, the hybrid algorithm (FFHSA) in combination of harmony search algorithm (HSA), and firefly algorithm (FFA) is also implemented for the optimal selection of adaptive neuro-fuzzy interface system (ANFIS) parameters to maintain direct current link capacitor voltage (DLCV) constant. The prime goal of the developed hybrid ANNC-FFHSA is to stabilize the DLCV with low settling time during load and solar irradiation (G), Temperature (T) changes, minimization of distortions in the source current signal to diminish total harmonic distortion (THD) in turn boosting the power factor (PF), suppression of fluctuations like disturbances, swell, sag and unbalances in the supply voltage. The suggested method is validated by four test cases with several combinations of variable irradiation (G), temperature and loads. On the other hand, to reveal the superiority of the developed method, the comparison is carried out with the genetic algorithm (GA) and Ant colony algorithm (ACA) along with instantaneous power (p–q) and Synchronous reference frame (SRF) conventional methods. The proposed approach significantly diminishes the total harmonic distortion to values of 3.61%, 3.48%, 3.48%, and 4.51%, which are notably lower compared to the values reported in the existing literature and also improves the power factor to almost unity. The design and implementation of this method were carried out using MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

To solve the flexible job shop dynamic scheduling problem with fuzzy quality control time, which is more realistic, this paper established two rescheduling models for machine breakdown and urgent order insertion with the objective of minimizing completion time. Two models were for three failure scenarios: emergency order insertion, machine failure during no-load operation, and machine failure during work-piece processing. To solve this model, this paper designed a novel genetic-neighborhood search algorithm that integrated cellular-automata-based neighborhood search and random restart hill-climbing. Aiming at the characteristics of dual sub-decisions involving order sequencing and machine selection, this paper designed a two-layer coding scheme including process code and machine code, and designed corresponding genetic operations such as crossover and mutation for them. After that, this paper applied the genetic operations into the framework of cellular-automata-based neighborhood search to enhance the algorithm's global search capability, and integrated the random restart hill-climbing algorithm based on key operations to improve the algorithms local development capability. Experiments tested 10 flexible job shop scheduling instances and one flexible job shop scheduling instance with fuzzy quality control time to verify the effectiveness of the proposed algorithm and models. In addition, the test results under different failure scenarios show the proposed method outperforms the backward-based scheduling strategy used in practice. [ABSTRACT FROM AUTHOR]

As software projects evolve and grow in size and complexity, so do their test suites. Test suite reduction (TSR) aims at reducing the size of a test suite by removing redundant and obsolete test cases based on a coverage metric while preserving its fault detection capabilities. The contributions of this paper are twofold: (1) We examine a lesser‐known coverage criterion, that is, checked coverage. Checked coverage not only investigates if a part of the code was executed but also if it was checked by a test oracle. In an empirical evaluation, we performed TSR based on different reduction algorithms, coverage metrics, and open‐source Java projects with our own TSR tool to determine the most effective and efficient combination of metric and method. (2) Given the results of the first evaluation, we further investigate the potential of parameter optimization in regard to a genetic reduction algorithm. In particular, we focus on finding a general setting for the parameters crossover rate and mutation rate such that test suites can be reduced in a reasonable time while maintaining a high fault detection power. [ABSTRACT FROM AUTHOR]

This paper investigates the Generalized Traveling Salesman Problem (GTSP), which is an extension of the well-known Traveling Salesman Problem (TSP), and it searches for an optimal tour in a clustered graph, such that every cluster is visited exactly once. In this paper, we describe a novel Memetic Algorithm (MA) for solving efficiently the GTSP. Our proposed MA has at its core a genetic algorithm (GA), completed by a Chromosome Enhancement Procedure (CEP), which is based on a TSP solver and the Shortest Path (SP) algorithm and for improving the convergence characteristics of the GA, a Local Search (LS) operation is applied for the best chromosomes in each generation. We tested our algorithm on a set of well-known instances from the literature and the achieved results prove that our novel memetic algorithm is highly competitive against the existing solution approaches from the specialized literature. [ABSTRACT FROM AUTHOR]

Dynamic voltage and frequency scaling (DVFS) is a tool used primarily to decrease computer processor energy consumption by lowering its operational frequency. Their only downside is that they distract from the efficiency of parallel applications while operating on parallel platforms. In a heterogeneous cluster architecture, however, a genetic algorithm is being implemented and applied to model the best trade-off between energy-saving and parallel application performance degradation. The proposed algorithm selects the best frequency vector in order to accomplish these objectives by providing the same compromise. So, the objective function of the genetic algorithm at the same time gives limited energy consumption and minimum decreases in performance. The SimGrid simulator will be used for all experiments. The suggested algorithm saves the average energy by (20 %) and the application performance degrades to the limit (0.15 %). [ABSTRACT FROM AUTHOR]

With the growth of online networks, understanding the intricate structure of communities has become vital. Traditional community detection algorithms, while effective to an extent, often fall short in complex systems. This study introduced a meta-heuristic approach for community detection that leveraged a memetic algorithm, combining genetic algorithms (GA) with the stochastic hill climbing (SHC) algorithm as a local optimization method to enhance modularity scores, which was a measure of the strength of community structure within a network. We conducted comprehensive experiments on five social network datasets (Zachary's Karate Club, Dolphin Social Network, Books About U.S. Politics, American College Football, and the Jazz Club Dataset). Also, we executed an ablation study based on modularity and convergence speed to determine the efficiency of local search. Our method outperformed other GA-based community detection methods, delivering higher maximum and average modularity scores, indicative of a superior detection of community structures. The effectiveness of local search was notable in its ability to accelerate convergence toward the global optimum. Our results not only demonstrated the algorithm's robustness across different network complexities but also underscored the significance of local search in achieving consistent and reliable modularity scores in community detection. [ABSTRACT FROM AUTHOR]

Efficient coordinated scheduling has long been a focal point in port research, complicated by the diverse optimization goals dictated by different port characteristics. This study focuses on Yangtze River ports, exploring coordinated scheduling amidst river–sea intermodal transportation. Our research aims to reduce berth deviation costs and shorten the total scheduling time for ships, while maximizing berth utilization rates for ports. Initially, we analyzed the operational realities of Yangtze River ports and waterways. Subsequently, we innovatively introduced three key factors influencing scheduling: berth preferences, seagoing ship inspections, and planning cycles. Finally we proposed the optimized Non-dominated Sorting Genetic Algorithm III (NSGA-III). Evaluating the model using a seven-day dataset of vessel activities at Yangtze River ports revealed significant improvements: the optimized NSGA-III enhanced objective values by 30.81%, 13.73%, and 12.11% compared to the original scheduling approach, surpassing both conventional NSGA-III and NSGA-II algorithms. This study underscores the model's efficacy in not only reducing operational costs through optimized ship and berth sequencing but also in enhancing clearance efficiency for relevant authorities. [ABSTRACT FROM AUTHOR]

Many-objective optimization presents unique challenges in balancing diversity and convergence of solutions. Traditional approaches struggle with this balance, leading to suboptimal solution distributions in the objective space especially at higher number of objectives. This necessitates the need for innovative strategies to adeptly manage these complexities. This study introduces a Many-Objective Artificial Hummingbird Algorithm (MaOAHA), an advanced evolutionary algorithm designed to overcome the limitations of existing many-objective optimization methods. The objectives are to improve convergence rates, maintain solution diversity, and achieve a uniform distribution in the objective space. MaOAHA implements information feedback mechanism (IFM), reference point-based selection and association, non-dominated sorting, and niche preservation. The IFM utilizes historical data from previous generations to inform the update process, thereby improving the algorithm's the exploration and exploitation capabilities. Reference point-based selection, along with non-dominated sorting, ensures solutions are both close to the Pareto front and evenly spread in the objective space. Niche preservation and density estimation strategies are employed to maintain diversity and prevent overcrowding. The comprehensive experimental analysis benchmarks MaOAHA against four leading algorithms viz. Many-Objective Gradient-Based Optimizer, Many-Objective Particle Swarm Optimizer, Reference Vector Guided Evolutionary Algorithm, and Nondominated Sorting Genetic Algorithm III. The DTLZ1–DTLZ7 benchmark sets with four, six, and eight objectives and five real-world problems (RWMaOP1–RWMaOP5) are considered for performance assessment of the selected algorithms. The results demonstrate that internal parameter-free MaOAHA significantly outperforms its counterparts, achieving better generational distance by up to 52.38%, inverse generational distance by up to 38.09%, spacing by up to 56%, spread by up to 71.42%, hypervolume by up to 44%, and runtime by up to 52%. These metrics affirm the MaOAHA's capability to enhance the decision-making processes through its adept balance of convergence, diversity, and uniformity. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deploying unmanned aerial vehicle base stations (UAV-BSs) to provide wireless access for terrestrial users is an alternative solution in emergency scenarios. Due to the dynamic nature of UAV-BSs and strong line-of-sight (LoS) transmission, co-frequency networking worsens the interference suffered by edge users, while the use of fully orthogonal channels for all UAV-BSs also faces the problem of frequency resource constraints. This paper develops a new deployment scheme by combining multiple UAV-BS locations, limited frequency reuse and power optimization. Thus, the nonlinear constrained optimization model is proposed to maximize user coverage. To reduce the computational complexity, the basic multi-UAV-BS network layout is first determined by polycentric clustering and connectivity adjustment based on user location distribution. Then, a genetic algorithm is used to optimize the multi-UAV-BS frequency arrangement and power adjustment to obtain the model solution. Simulations verify the effectiveness of the proposed scheme and evaluate the impact of co-channel interference. [ABSTRACT FROM AUTHOR]

With the rapid development of heterogeneous network technologies, such as mobile edge computing, satellite communications, self-organizing networks, and the wired Internet, satisfying users' increasingly diversified and complex communication needs in dynamic and evolving network environments has become a critical research topic. Ensuring secure and reliable information transmission is essential for stable network operation in these complex environments. Addressing this challenge, this study proposed a secure and reliable multi-objective optimized multipath transmission algorithm for heterogeneous networks to enhance security and reliability during data transmission. The core principle of this algorithm was that multipath transmission can provide additional protection through redundant paths. This redundancy ensured that even if one path is attacked or fails, alternative paths can maintain data integrity and reachability. In this study, we employed the Optimized Non-dominated Sorting Genetic Algorithm II (ONSGA-II) to determine the range of the initial population and filter suitable paths by optimizing them according to different demand objectives. In the path selection process, we introduced an innovative deletion graph method, which ensures that redundant paths do not share any common links with the original paths, except when there are unique links. This approach enhances the independence of transmission paths and improves the security of the transmission process. It effectively protects against security threats such as single points of failure and link attacks. We have verified the effectiveness of the algorithm through a series of experiments, and the proposed algorithm can provide decision-makers with high-reliability and low-latency transmission paths in heterogeneous network environments. At the same time, we verified the performance of the algorithm when encountering attacks, which is superior to other classical algorithms. Even in the face of network failures and attacks, it can maintain a high level of data integrity and security. [ABSTRACT FROM AUTHOR]

Novel vertical take-off and landing advanced air mobility aircraft which are overactuated and transition between vertical and forward flight modes pose unique challenges for the design of safe and robust full-envelope fly-by-wire flight control systems. This paper presents a methodology for designing and optimizing a control system architecture for a tilt-wing urban air mobility concept. It features the total energy control system algorithm, which is extended to be applicable to hover and transitioning flight and explicit model following inner loops. Control system parameters are optimized using a genetic algorithm optimization scheme, subject to constraints on closed-loop dynamic stability and control response characteristics. Control system performance is demonstrated by simulation of lateral, longitudinal, and directional maneuvering for hover, transition, and forward flight conditions, followed by simulation of departure and arrival transitions while tracking a prescribed flightpath and speed profile in calm and turbulent air. The results demonstrate the effectiveness of the proposed control system architecture and the demonstrated optimization methodology. [ABSTRACT FROM AUTHOR]

An improved genetic algorithm is proposed to optimize the deep neural network algorithm for visual style conversion in visual media. It consists of two parts: optimizing the deep neural network algorithm design and designing a video style conversion model. The genetic algorithm selection strategy is enhanced to optimize the neural network structure. A non-recursive neural network is used to handle temporal inconsistency in a single frame. Experimental results on the Heart dataset show that the accuracy of the optimized deep neural network algorithm is 0.8913, outperforming other algorithms like the generative adversarial dual neural network (0.8696), ant colony optimization (0.8651), active network (0.8536), genetic algorithm (0.8566), and particle swarm algorithm (0.8558). Moreover, the optimized algorithm achieves high temporal stability and running speed in single and multi-style conversion networks. In conclusion, the proposed strategy using improved genetic algorithms to optimize deep neural network algorithms for visual style conversion offers effective solutions with high application value in terms of accuracy, temporal stability, and running speed. [ABSTRACT FROM AUTHOR]

As the real estate industry expands with time, the personalized needs of users for indoor space layouts have become increasingly complex. Traditional indoor space layout design methods can no longer meet the needs of large market groups because of their complex steps and low levels of specialization. Therefore, this study first analyzes the problematic factors in indoor space layout design. Second, an interactive genetic algorithm is introduced to solve the multifactor optimal selection problem; the process is optimized and improved using a differential evolution algorithm. A comprehensive spatial layout model combining interactive genetic and differential evolution algorithms is proposed. The experimental results show that the model performs best with uniform variation, and its average number of iterations to find the optimal individual is 57. In addition, compared with similar layout models, the proposed model achieved the highest space utilization value of 79%, which is approximately 19% higher than that for the stacking layout model; it also required the shortest time, that is, 15 min. In summary, the proposed model provides a new intelligent method for indoor layout design, which is expected to improve the satisfaction of designers and users. [ABSTRACT FROM AUTHOR]

Driven by Chinese double carbon policy, logistics companies should ensure both environmental preservation and fluency in the last-mile delivery. With the objective of minimizing the total distribution distance, a model for location of mobile recharge stations and the route planning is formulated, which considers some constraints such as load, power and service capacity of recharge stations. Subsequently, a two-stage algorithm is proposed. The genetic algorithm is used to generate the initial routing plans in the first stage, and the RSI algorithm is designed to locate recharge stations and adjust routing plans in the second stage. Tested by the CVRP benchmark cases, the results show that the average increasing rate of travel distances caused by visiting recharge station is less than 5%. Compared with PSO and SA, the results between the proposed algorithm and the other two algorithms are respectively -4.04% and -3.65%. Also, the adaptability of the model is verified through the sensitivity analysis with the main model parameters such as power consumption rate. Therefore, the mode is feasible if logistics companies can afford to use exclusive mobile recharge stations and accept the increase of travel distances which is less than 8%. [ABSTRACT FROM AUTHOR]

Community detection in complex networks plays a pivotal role in identifying the structural composition of nodes, enabling us to pinpoint key topological features critical for a multitude of applications. Usually, the complex networks are in graphical format in which graph nodes denote objects and edges denote a connection between two things. The existing techniques have limitations such as early convergence and local optima issues. To overcome this issue, this manuscript proposed an Improved Hybrid Harris Hawk and Coot Bird Optimization (IHHHCBO) algorithm in community detection. The optimum separate movement of CBO is incorporated in HHO population initialization to strengthen both optimizations. It is employed to initialize the population for enhancing the position of diversity and change with other individuals. The Karate, Dolphin, Football and Political Books datasets are considered for assessing the IHHHCBO performance. The Ensemble Mutation Strategy (EMS) is developed to produce mutant candidate locations which enhances the exploration and population diversity of optimization. The Normalized Mutual Information (NMI), and Modularity (Q) are considered fitness functions in this research. The IHHHCBO performance is estimated through metrics like NMI, Q, f1-score and accuracy. The IHHHCBO reaches better accuracy of 1, 1, 0.998 and 0.883 for Karate, Dolphin, Football and Political Books respectively which is better when compared to existing algorithms like Core Structure Extraction Algorithm (CSEA), and Modified Crossover Opposition-based Genetic Algorithm (MCOBGA). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To enhance the interactivity of spatial design, this study proposes an indoor spatial layout design method based on differential evolution algorithm, which combines backtracking strategy and reverse learning strategy to improve the interactive differential evolution algorithm. The experimental data demonstrated that the proposed method for indoor space layout design achieved an average user satisfaction of 81.7%, which increased by 16.8% compared with traditional interactive genetic algorithms. In addition, the improved human-computer interaction interface scored higher than 0.8 in terms of usability, reliability, customizability, and interactive feedback. This means that the improved interface can better meet user needs and provide a better user experience. This study shows that the indoor space layout design method ground on differential evolution algorithm and the improved human-computer interaction interface can significantly improve user satisfaction and user experience. This has brought more efficient and convenient solutions to the field of spatial design. [ABSTRACT FROM AUTHOR]

To improve the task dispatch plan for vehicular optical observation equipment, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed. Firstly, the task dispatch problem is modeled with the constraints and optimum indexes. To deal with multiple optimum indexes, the optimum index which is not considered in the objective function is considered as a constraint. Secondly, the multi-objective fitness function and selection operator is calculated based on the fast non-dominated sorting method of NSGA-II. The Pare to solution set obtained by the multi-objective optimization is the task dispatch plan solution. Finally, the proposed algorithm is verified by a simulation example. The simulation results show that this method can solve the task dispatch plan problem effectively, which is valuable for the application of the vehicular optical observation equipment. [ABSTRACT FROM AUTHOR]

Thrust allocation (TA) plays a critical role in the dynamic positioning system (DPS). The task of TA is to allocate the rotational speed and angle of each thruster to generate the generalized control forces. Most studies take TA as a single-objective optimization problem; however, TA is a multi-objective optimization problem (MOP), which needs to satisfy multiple conflicting allocation objectives simultaneously. This study proposes an improved multi-objective particle swarm optimization (IMOPSO) method to deal with the non-convex MOP of TA. The objective functions of reducing the allocation error, and minimizing the power consumption and the tear-and-wear of thrusters under physical constraints, are established and solved via MOPSO. To enhance the global seeking ability, the improved mutation strategy combined with the roulette wheel mechanism is adopted. It is shown through test data that IMOPSO converges better than multi-objective algorithms such as MOPSO and nondominated sorting genetic algorithm II (NSGA-II). Simulations are conducted for a DP ship with two propeller–rudder combinations. The simulation results with the single-objective PSO algorithm show that the proposed IMOPSO algorithm reduces thrust allocation errors in the three directions of surge, sway, and yaw by 48.48%, 39.64%, and 15.02%, respectively, and reduces power consumption by 44.53%, which demonstrates the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a solution method for the multimission selective maintenance problem (SMP) that combines column-generation (CG) and genetic algorithms (GAs). The multimission SMP is an optimisation problem that arises when a system performs a sequence of missions separated by breaks of finite duration. During these finite breaks, only a subset of possible maintenance actions can be performed due to resource limitations. The problem is in deciding what actions to perform during each break duration such that the system meets or exceeds a minimum target reliability for all missions. The resulting optimisation problems are usually modelled as mixed integer nonlinear mathematical programmes, which are hard to solve. They are usually solved using metaheuristics. We propose a solution method based on CG framework in which the subproblems are solved using a GA. By integrating the GA within the classical CG framework, high-quality solutions can be obtained very quickly. The proposed solution method is capable of solving systems composed of both parallel and k-out-of-n:G subsystems. This hybrid CG algorithm is shown to obtain near optimal solutions and outperform other metaheuristic solution methods; it is also shown to be capable of solving large-scale systems composed of many subsystems and hundreds of components in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region. It establishes a "prediction-scheduling" search strategy model, planning a rapid search task for disconnected targets based on overlapping probability through the IAIGA. By incorporating an immune mechanism, the algorithm enhances its global search capability and robustness. Additionally, the adaptive strategy enables dynamic adjustment of the algorithm's parameters to accommodate varying search scenarios. The experimental results demonstrate that the proposed IAIGA significantly outperforms traditional methods, providing higher search speeds and more accurate search results in the context of maritime emergency response. These findings offer effective technical support for maritime emergency operations. [ABSTRACT FROM AUTHOR]

Ensuring optimal load balancing is imperative for maintaining reliability and upholding quality of service as specified in service-level agreements (SLAs) for cloud computing providers. This research addresses the most common shortcomings of existing state-of-the-art methods, which often lack responsiveness and struggle to adapt to exponentially increasing demand, especially in the era of the internet of things (IoT). The proposed hybrid approach surpasses current literature approaches in performance metrics such as makespan, response time, number of cloudlet migrations, and SLA violations. It operates on two levels, initially employing a k-means clustering algorithm to group servers within each datacenter based on similar utilization rates. Subsequently, a round-robin method allocates task groups sequentially to non-overloaded clusters, and within each cluster, a genetic algorithm optimally assigns tasks to servers. This multilayered approach facilitates hot-deployment and scalability in operational cloud environments while promoting strong interoperability and decoupling of core mechanisms missions. Simulation experiments conducted on CloudSim Plus validate the superiority of our method, positioning it as a robust solution for enhancing load balancing and tasks scheduling in cloud environments, especially in the face of rapidly increasing IoT-related demands. [ABSTRACT FROM AUTHOR]