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This paper emphasises the pivotal role of enhancing research and development (R&D) staff competence and boosting team innovation efficiency in configuring R&D project teams for artificial intelligence (AI) product development. Diverging from traditional studies primarily focused on time, quality, and cost objectives, this study proposes prioritising the skill increments of staff and team diversity, aligning with the overarching goals of the R&D cycle. Targeting multi-skilled R&D personnel for scheduling and configuration, a three-objective tradeoff optimisation model is established. The nonlinear mixed-integer constrained programming model incorporates a learning effect for calculating employee skill value and employs a heterogeneity efficiency formula for assessing team diversity, particularly emphasising the diversity of R&D personnel research backgrounds. An algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to obtain the approximate Pareto optimal solution. Furthermore, it compares the algorithm with the multi-objective particle swarm optimisation (MOPSO), explores practical decision-making methods for Pareto solutions, and conducts sensitivity analyses on the learning rate and diversity level. Our research is relevant to enterprises seeking to enhance R&D capabilities with a certain degree of homogeneity among R&D employees. This paper exemplifies and validates the proposed model and solution approach using a new AI product from a healthcare company. [ABSTRACT FROM AUTHOR]

With the service level playing an increasingly essential role in the service-oriented manufacturing (SOM) supply chain, the distribution network design can be heavily affected by the customer behaviour based on their satisfaction of services. In this paper, we consider the service level for service time and delivery quantity separately. A novel mixed integer non-linear programming model is proposed to design the multi-period distribution network from a fourth-party logistics (4PL) perspective. The customer satisfaction based on prospect theory is maximised while considering the investment budget and the service level. A scenario-based linear reformulation is proposed to find the optimal solution when the problem scale is small. For a large-scale problem, we propose an individual-driven Q-learning based memetic particle swarm optimisation algorithm. Numerical experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm. Furthermore, the impact of service modes, different customer behaviour, and customer satisfaction evaluation periods on distribution network is investigated. We find that the length of evaluation periods leads to differences in customer satisfaction due to different perceptions of 'small loss' and 'big gain' by boundedly rational customers. [ABSTRACT FROM AUTHOR]

After the expiration of each lease, lessees may return leased equipment on schedule, renew the lease or purchase it according to the equipment status and their demands. By considering lessees' uncertain options and equipment status difference, the pricing and inventory decision-makings of leased equipment are explored. A mixed-integer nonlinear programming model by maximising the net present value of lessor's profit is presented with respect to constraints like rental revenue, manufacturing, lessees' credit check, transportation, maintenance, upgrade and inventory costs. The rental price and inventory decisions are obtained by solving the problem with a particle swarm optimisation algorithm. We also analyse the impacts of purchasing cost of old equipment from a third-party supplier on lessor's inventory, renewal price or purchasing price of leased equipment on lessees' options and lessor's profit. The results show: (1) with the extension of lease period, the rental price increases while the growth rate decreases; (2) the maintenance cost accounts for about 20% of total cost, and the preventive maintenance strategy can reduce excess maintenance cost as lease period increases; (3) the lessor shall set moderate renewal price discount coefficient and purchasing price coefficient, and analyse purchasing cost of old equipment to manage inventory timely. [ABSTRACT FROM AUTHOR]

The recent discovery of MoB2 with a superconducting transition temperature (Tc) of up to 32 K at 100 GPa provides new insights into the metallization and subsequently high-Tc superconductivity of diborides, highlighting the potential of transition metals in these compounds. We herein re-evaluated the structure, mechanical, and superconducting properties of IrB4 under pressure up to 300 GPa using first-principles. Our calculations reveal that a new P21/c phase exhibiting a hardness of 15.75 GPa surpasses the stability of the C2/m structure identified through the particle swarm optimization at ambient pressure. Upon compressing, the P21/c phase transforms into an MgB2-type structure with a space group of P63/mmc at 62.5 GPa and then into the orthorhombic Cmca phase above 109 GPa. Unlike semiconductor behavior of the atmospheric pressure phase, the two high-pressure structures are metallic and superconducting, with Tc values of 29.90 for P63/mmc at 62.5 GPa and 13.45 K for Cmca at 125 GPa. Analysis of the electronic structure and electron–phonon coupling (EPC) reveals that the high Tc, similar to MgB2-type MoB2, stems from the Van Hove Singularities (VHS) near the Fermi level donated by transition metal Ir. The effect further enhances the EPC based on the boron contribution. More interestingly, pressure has little impacts on the position of the VHS. These findings provide a new platform for designing advanced high-Tc superconductors. [ABSTRACT FROM AUTHOR]

The recent discovery of MoB2 with a superconducting transition temperature (Tc) of up to 32 K at 100 GPa provides new insights into the metallization and subsequently high-Tc superconductivity of diborides, highlighting the potential of transition metals in these compounds. We herein re-evaluated the structure, mechanical, and superconducting properties of IrB4 under pressure up to 300 GPa using first-principles. Our calculations reveal that a new P21/c phase exhibiting a hardness of 15.75 GPa surpasses the stability of the C2/m structure identified through the particle swarm optimization at ambient pressure. Upon compressing, the P21/c phase transforms into an MgB2-type structure with a space group of P63/mmc at 62.5 GPa and then into the orthorhombic Cmca phase above 109 GPa. Unlike semiconductor behavior of the atmospheric pressure phase, the two high-pressure structures are metallic and superconducting, with Tc values of 29.90 for P63/mmc at 62.5 GPa and 13.45 K for Cmca at 125 GPa. Analysis of the electronic structure and electron–phonon coupling (EPC) reveals that the high Tc, similar to MgB2-type MoB2, stems from the Van Hove Singularities (VHS) near the Fermi level donated by transition metal Ir. The effect further enhances the EPC based on the boron contribution. More interestingly, pressure has little impacts on the position of the VHS. These findings provide a new platform for designing advanced high-Tc superconductors. [ABSTRACT FROM AUTHOR]

As a high-energy density material, polymeric nitrogen has attracted considerable attention, while the exceptionally high synthesis pressure hinders its studies and applications. A significant discovery indicates that the insertion of noble gas elements can effectively reduce the synthesis pressure of polymeric nitrogen compounds. This work utilized the particle swarm optimization algorithm and first-principles calculations to extensively explore the stoichiometry of Xe–N compounds under high pressures. Two phases of XeN14, P6mm and P-62m, have been discovered, which are energetically more stable than the basic mixture of Xe and N2. Evidence of charge transfer between Xe and N was found, verifying that Xe plays a crucial role in forming polymeric nitrogen compounds. These two compounds are kinetically stable at pressures ranging from 50 to 200 GPa and exhibit semiconductor properties. A unique channel-like structure was discovered in the P6mm phase. The energy densities of P6mm and P-62m phases are 7.39 and 7.59 kJ/g, respectively, significantly exceeding those of TNT (Trinitrotoluene) and HMX (Octogen), indicating their potential as high-energy density materials. [ABSTRACT FROM AUTHOR]

Using the particle swarm optimization algorithm, we conducted an extensive search for the high-pressure stable structure of technetium diboride (TcB2) within the pressure range of 0–400 GPa. At zero pressure, the P63/mmc (hP6-TcB2) structure is considered the ground state configuration. As the pressure increases, a structural transition from hP6-TcB2 to P6/mmm (hP3-TcB2) occurs at approximately 174.9 GPa. We discuss the bonding between the two distinct phases and analyze the contribution of different atomic bonds to maintaining their structural stability. Meanwhile, the temperature–pressure phase diagram of TcB2 was successfully determined for the first time through the quasi-harmonic approximation method. It is predicted that the transition pressure from hP6-TcB2 to hP3-TcB2 can be reduced to about 164 GPa at a room temperature of 300 K. These results provide valuable insights into the behavior of TcB2 under different temperature and pressure conditions and open up new possibilities for exploring its potential applications in a variety of environments. [ABSTRACT FROM AUTHOR]

Changes to components, known as engineering changes (ECs), rarely occur on their own. In fact, in complex assembly systems, most ECs are introduced in batches to ensure that changed components match. As a result, to implement ECs optimally, multiple component's stock must be considered until the change is executed on the EC effectivity date. This problem is known as the EC effectivity date optimisation problem, a variation of the general inventory control problem with deterministic and dynamic demand. As optimisation and monitoring of this problem is computationally expensive, research has suggested to investigate whether metaheuristics can provide adequate support. To fill this research gap, we present the results of a benchmark on basic metaheuristics for EC effectivity date optimisation. To do so, we have compared five common metaheuristics in their basic form (Ant Colony Optimisation, Genetic Algorithm, Particle Swarm Optimisation, Tabu Search, and Simulated Annealing) on a real-world test set. Of the tested algorithms the Genetic Algorithm identified most best solutions and returned good average results for the test cases. However, as its reliability was comparatively low, our research suggests a sequential application of the Genetic Algorithm and Tabu Search. [ABSTRACT FROM AUTHOR]

With the fierce competition in e-commerce, e-tailers are required to rapid responses to a variety of customised orders with multiple frequencies and strict delivery times. The delay or insufficient supply caused by disruptions might result in lost sales during long-term processes. To address this problem, a two-stage stochastic programming model considering profits, consumer service level (CSL) as well as market priorities is developed to manage long-term disruptions. We analyse multi-period consumer transaction data and formulate geographical relevance (GR) to link each marketplace with historical data in related regions and then prioritise market segments. A GR-based two-stage stochastic programming with multi-period is proposed, which (1) considers both proactive mitigation decisions before disruption and reactive recovery plans after disruption; (2) collaborates three resilience strategies; (3) optimises the e-tailer's profits considering market priorities during long-term disruptions. Using a real case of Chinese e-commerce under the COVID-19 pandemic, it is illustrated (1) the applicability and performance of the proposed GR-based model for multi-period resilience optimisation improving both the CSL and the total profit; (2) the efficiency and robustness of the developed sequential particle swarm optimisation with social structures algorithm. The proposed method could optimise e-tailers' response strategies for managing long-term disruptions in practice. [ABSTRACT FROM AUTHOR]

This paper proposes a white-box perspective that portrays a supply chain delivery process as a network of related activities which remains to be improved. It addresses a critical disadvantage of supply chain delivery performance models, namely considering a delivery process as a whole and ignoring characteristics and relationships between activities in the delivery process. A delivery process is modeled using the Graphical Evaluation and Review Technique based on the characteristic function (CF-GERT). Based on the CF-GERT model, a framework for applying managerial effort to activities to improve overall delivery performance is proposed. Then, particle swarm optimization (PSO) based on the penalty function is used to solve the delivery performance improvement framework. Finally, a numerical case shows how applying efforts to activities can effectively improve delivery performance and demonstrates the influence of several parameters on the related costs. [ABSTRACT FROM AUTHOR]

Groundwater aquifers worldwide experience unsustainable depletion, compounded by population growth, economic development, and climate forcing. Managed aquifer recharge provides one tool to alleviate flood risk and replenish groundwater. However, concerns grow that intentional flooding of farmland for groundwater recharge, a practice known as Ag-MAR, may increase the leaching of pesticides and other chemicals into groundwater. This study employs a physically based unsaturated flow model to determine the fate and transport of residues of four pesticide in three vadose zone profiles characterized by differing fractions of sand (41 %, 61 %, and 84 %) in California's Central Valley. Here, we show that the complex heterogeneity of alternating coarse and fine-grain hydrogeologic units controls the transit times of pesticides and their adsorption and degradation rates. Unsaturated zones that contain a higher fraction of sand are more prone to support preferential flow, higher recharge rates (+8 %), and faster (42 %) water flow and pesticide transport, more flooding-induced pesticide leaching (about 22 %), as well as more salt leaching correlating with increased risks of groundwater contamination. Interestingly, considering preferential flow predicted higher degradation and retention rates despite shorter travel times, attributed to the trapping of pesticides in immobile zones where they degrade more effectively. The findings underscore the importance of considering soil texture and structure in Ag-MAR practices to minimize environmental risks while enhancing groundwater recharge. The study also highlights that selecting less mobile pesticides can reduce leaching risks in sandy areas.

Understanding interparticle interactions has been one of the most important topics of research in the field of micro/nanoscale materials. Many significant characteristics of such materials directly stem from the way their building blocks interact with each other. In this work, we investigate the efficacy of a specific category of Machine Learning (ML) methods known as interaction networks in predicting interparticle interactions within colloidal systems. We introduce and study Local Neighborhood Graph Neural Networks (LN-GNNs), defined according to the local environment of colloidal particles derived from particle trajectory data. The LN-GNN framework is trained for unique categories of particle neighborhood environments in order to predict interparticle interactions. We compare the performance of the LN-GNN to a baseline interaction network with a simpler architecture and to an Instance-Based ML algorithm, which is computationally more expensive. We find that the prediction performance of LN-GNN measured as an average normalized mean absolute error outperforms the baseline interaction network by a factor of 2–10 for different local neighborhood configurations. Furthermore, LN-GNN's performance turns out to be very comparable to the instance-based ML framework while being an order of magnitude less expensive in terms of the required computation time. The results of this work can provide the foundations for establishing accurate models of colloidal particle interactions that are derived from real particle trajectory data. [ABSTRACT FROM AUTHOR]

The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute, through the strategic synergy of multi-fidelity evaluations, machine learning models, and optimization algorithms. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. It leverages a machine learning model trained with low-fidelity simulation data, in each optimization cycle, thereby proficiently predicting a target variable and discerning whether a high-fidelity simulation is necessitated, which notably conserves computational resources. Additionally, the machine learning model is strategically deployed prior to optimization to compress the design space boundaries, thereby further accelerating convergence toward the optimal solution. The methodology has been employed to enhance two optimization algorithms, namely Differential Evolution and Particle Swarm Optimization. Comparative analyses illustrate performance improvements across both algorithms. Notably, this method is adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.

Summary: Relay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach. [ABSTRACT FROM AUTHOR]

Quasi-zero-stiffness (QZS) isolator has great application potential in the field of low-frequency vibration isolation due to its high-static and low-dynamic (HSLD) nonlinear stiffness characteristic, but it is precisely this characteristic that makes it very sensitive to load changes. Once the load changes, causing it to deviate from the equilibrium position, it no longer qualifies zero-stiffness characteristic, and the vibration isolation capacity will be significantly decrease. To make the QZS isolator possess variable load capacity and be more suitable for engineering practice, an active QZS vibration isolator based on electromagnetic actuator is designed in this paper, which eliminates the influence of load changes through the active force of the electromagnetic actuator. First, the dynamic equation of the isolator is established by Newton–Euler method, and the dynamic characteristic of the isolator under standard load and load variations are analyzed through improved incremental harmonic balance (IHB) method based on discrete Fourier transform (DFT). Next, the improved particle swarm optimization (PSO) algorithm are employed to optimize the Proportion Integration Differentiation (PID) controller parameters. Then, the vibration isolation performance of QZS isolator in controlled and uncontrolled and linear systems in the same control state are compared in frequency domain and time domain, respectively. Finally, the performance of active QZS isolator under load variation is discussed. The results indicate that the isolation performance of the QZS isolator under active control is significantly better than in uncontrolled conditions and the controlled linear system. When the load changes, real-time compensation through the actuator output control force can also enable the QZS isolator to achieve a better vibration isolation performance. [ABSTRACT FROM AUTHOR]

Face recognition is used in many applications such as access control, automobile security, criminal identification, immigration, healthcare, cyber security, and so on. Each person has his/her own unique face, so the face can help distinguish people from each other. Feature extraction process plays a fundamental role in accuracy of face recognition, and many algorithms have been presented to extract more informative features from the face image. In this paper, an efficient texture descriptor is proposed based on local information of the face image. In the proposed method, at first, face image is split into several sub-images in such a way that each sub-image includes one of the facial parts such as eyes, nose, and lips. Second, texture features are extracted from each sub-image using a new local pattern descriptor, and then features of sub-images are concatenated to construct feature vector. Finally, the face image is compared to images in a dataset based on a similarity measure. In addition, particle swarm optimization algorithm is used to assign weight to the features of different parts of the face image. To evaluate the proposed algorithm, four face datasets, Yale, ORL, GT and KDEF, are used. Implementation results show that the proposed method outperforms recent methods in terms of accuracy, receiver operating characteristic (ROC) curve, and area under ROC curve. [ABSTRACT FROM AUTHOR]

The aircraft landing problem (ALP) is a challenging scheduling and optimization problem in the industry and engineering, which has attracted attention in recent decades. Existing research has predominantly concentrated on optimizing aircraft delay and the financial implications of early or late landings. However, given the paramount significance of airport fuel costs at airports and the critical need for efficient fuel utilization, we aim to minimize airplane fuel consumption by streamlining operational time. In this paper, we present an innovative model with two main objectives: minimizing airplane fuel consumption by reducing dwell time and minimizing cost operation. To address these dual objectives concurrently, we propose a new method known as the multi populations of multiple objectives (MPMO) framework, which is modeled through a non-dominated sorting genetic algorithm-II (NSGA-II) called MPNSGA-II. First, MPNSGA-II employs two separate populations to optimize each objective. Second, to prevent populations from fixating solely on their respective single objectives, MPNSGA-II introduces an archive sharing strategy (ASS). This technique stores elite solutions gathered from two populations. Additionally, we introduce an archive update strategy (AUS) to enhance the quality of solutions stored in the archive. The proposed algorithm has been compared with other well-known algorithms, NSGA-II, multi-objective particle swarm optimization (MOPSO), and NSGA-III. The proposed algorithm shows a cost reduction in 18.01%, 16.75%, and 15.21%. Statistical precision, underscored through the application of the nonparametric Friedman test, corroborates the supremacy of the proposed method, clinching the highest ranking compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms have been widely employed to solve data-driven optimization problems. However, for offline data-driven optimization, it is very challenging to perform evolutionary search efficiently as well as accurately since no new data is available during the optimization process. To mitigate this issue, a multifidelity surrogates-assisted multi-objective particle swarm optimization (MFSa-PSO) algorithm is proposed in this paper. First, two low-fidelity models with convergence and diversity characteristics separately and a high-fidelity model are constructed to assemble multifidelity surrogate models. Second, by adopting the knowledge transfer strategy, the multifidelity surrogates-assisted two-archive multi-objective particle swarm optimization is conducted to search optimal solutions more exactly and effectively. Third, the output solution set is achieved by associating the solutions of two archives with reference vectors. Finally, the proposed MFSa-PSO is compared with some popular surrogate-assisted evolutionary algorithms on benchmark problems to verify its effectiveness and outperformance. Additionally, a real-world application of the municipal solid waste incineration process is carried out to verify the engineering applicability of MFSa-PSO. [ABSTRACT FROM AUTHOR]

One‐dimensional thrust vectoring turbojet vertical takeoff and landing (VTOL) aircraft have attracted research attention due to their high thrust‐to‐weight ratios and their solution for the slow speed response of turbojet engines. However, there are some uncertainties and physical limitations in their systems, such as external disturbances, unknown parameters and input saturation. To improve the accuracy and convergence speed of trajectory tracking, a fixed‐time control method that is robust to saturation is proposed. The system dynamics are established, and an auxiliary system is built within a fixed time control framework to address the influence of input saturation and increase the error convergence rate. Nonsingular fast terminal sliding mode technology is combined with a few adaptive laws to ensure the robustness of the closed‐loop system against dynamic uncertainties and improve the precision of steady‐state control. The stability of the control system is proven based on Lyapunov, and the controller parameters are optimized based on particle swarm optimization (PSO). The proposed method based on fixed‐time stability guarantees that the states of the closed‐loop system can reach the residual set around zero within the designed time, and an expression for the upper bound on the convergence time is given. Finally, numerical simulations demonstrate the superiority of the proposed method based on the error integral criterion. [ABSTRACT FROM AUTHOR]

This article aims to address the issue of low recognition accuracy in existing sorting robots caused by lighting, occlusion, and environmental factors. A fruit recognition method based on a flexible tactile sensor array is described. This method enables the robot to directly perceive the attributes of the objects and identify fruits using a flexible gripper, facilitating intelligent sorting. A novel flexible tactile sensor array is utilized to construct a flexible hand tactile information acquisition platform, which collects tactile time series data for the fruits. Principal component analysis is then employed for dimensionality reduction, followed by the development of an improved particle swarm optimization for the support vector machine model. Through an experimental study, the optimized model is compared with four other models, demonstrating better classification performance. The optimized model achieves an average tactile classification accuracy of up to 98.10% for the five types of fruits. A comparison between the improved optimization algorithm, genetic algorithm, and grid search algorithm reveals the superior optimization performance of the new approach. In the future, this method is expected to be implemented in industrial sorting robots for intelligent sorting on automatic production lines. Furthermore, the algorithm will be further refined to enhance the classification accuracy and efficiency. [ABSTRACT FROM AUTHOR]

A cloud load balancer should be proficient to modify it's approach to handle the various task kinds and the dynamic environment. In order to prevent situations where computing resources are excess or underutilized, an efficient task scheduling system is always necessary for optimum or efficient utilization of resources in cloud computing. Task Scheduling can be thought of as an optimization problem. As task scheduling in the cloud is an NP-Complete problem, the best solution cannot be found using gradient-based methods that look for optimal solutions to NP-Complete problems in a reasonable amount of time. Therefore, the task scheduling problem should be solved using evolutionary and meta-heuristic techniques. This study proposes a novel approach to task scheduling using the Cuckoo Optimization algorithm. With this approach, the load is effectively distributed among the virtual machines that are available, all the while keeping the total response time and average task processing time(PT) low. The comparative simulation results show that the proposed strategy performs better than state-of-the-art techniques such as Particle Swarm optimization, Ant Colony optimization, Genetic Algorithm and Stochastic Hill Climbing. [ABSTRACT FROM AUTHOR]

Today, with the increasing expansion of IoT devices and the growing number of user requests, processing their demands in computational environments has become increasingly challenging.The large volume of user requests and the appropriate distribution of tasks among computational resources often result in disordered energy consumption and increased latency. The correct allocation of resources and reducing energy consumption in fog computing are still significant challenges in this field. Improving resource management methods can provide better services for users. In this article, with the aim of more efficient allocation of resources and service activation management, the metaheuristic algorithm CSO (Cat Swarm Optimization) is used. User requests are received by a request evaluator, prioritized, and efficiently executed using the container live migration technique on fog resources. The container live migration technique leads to the migration of services and their better placement on fog resources, avoiding unnecessary activation of physical resources. The proposed method uses a resource manager to identify and classify available resources, aiming to determine the initial capacity of physical fog resources. The performance of the proposed method has been tested and evaluated using six metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization, Grasshopper Optimization algorithm, Genetic algorithm, Cuckoo Optimization algorithm, and Gray Wolf Optimization, within iFogSim. The proposed method has shown superior efficiency in energy consumption, execution time, latency, and network lifetime compared to other algorithms. [ABSTRACT FROM AUTHOR]

Efficiently scanning for space signals and accurately detecting them from noisy environment is essential in space communication. Various unwanted interferences also present in space that may hamper the perfect detection process. This paper proposes a novel near-field circular beamformer (NCB) that will perfectly detect the desired source signal from any direction and position in space. To improve the robustness of NCB against Direction of Arrival (DOA) error, distance error, unwanted interferences and noises, this work also offers robust NCBs (RNCB) using robust Optimal Diagonal Loading (ODL) and Variable Diagonal Loading (VDL) techniques. While searching for wanted signal, the beamformer provides a primary lobe at the look direction and shows some secondary unwanted side lobes for noise and interference. Sometimes these undesired side lobe levels (SLL) become so severe that it may create conflict in locating the precise position of the desired source. To reduce these SLL, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) techniques have been applied to RNCB. The simulation results show that the optimized RNCB significantly diminishes the objectionable SLL of non-optimized RNCB by choosing appropriate weight vector of antenna array without affecting the other antenna parameters. Artificial Neural Network (ANN) have also been used to predict the weight vector for minimum SLL. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Quality & Reliability Engineering International. The issue showcases original research presented at the 2023 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering. The research papers cover a diverse range of topics related to reliability and safety in modern engineering systems. The papers address areas such as reliability modeling and analysis, reliability-based design and optimization, failure/safety analysis and prevention, and maintainability. The authors express their appreciation to the journal and the reviewers for their support and hope that the research presented will be valuable to researchers, engineers, scientists, and students in the field. [Extracted from the article]

Optimization has been a field of interest in science and engineering and many metaheuristic algorithms have been developed and applied to various problems. These, however, often require parameter adjustments to achieve a suitable performance. This paper proposes a new framework to improve the performance of metaheuristics, termed Multi-Stage Parameter Adjustment (MSPA), which integrates Metaheuristics, an efficient sampling approach, and Machine Learning. The sampling method utilized here known as Extreme Latin Hypercube Sampling (XLHS) is used to divide parameter spaces into equally probable subspaces, ensuring better coverage due to the continuous nature of variables. These parameters are then improved through a primary optimizer for different numbers of variables using a selected benchmark problem. The resultant data are utilized to train an artificial neural network (ANN). The adjusted metaheuristic algorithm is subsequently employed for structural optimization. In this respect, the input data for the ANN comprise the average of the lower and upper bounds of each subspace and the number of variables, while output data are the optimized values obtained using the Primary Optimizer, which does not require extensive parameter adjustments. To evaluate the efficiency of the proposed framework in comparison with the original version and some other algorithms in the literature, the parameters of Particle Swarm Optimization, chosen for its widespread applicability, are adjusted and tested against some mathematical benchmarks, two engineering, and two truss structural optimization problems. Results demonstrate the efficacy of the presented framework in enhancing the performance of metaheuristic algorithms, particularly in the optimal design of truss structures. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and large torque ripple of switched reluctance motor, a cascaded fractional order PID sliding mode control system is designed. Considering the difficulty of parameter adjustment caused by the complexity of the system, the optimization technology using dung beetle optimizer is introduced to optimize the designed cascaded controller and control the speed and torque ripple. To prove the effectiveness of the proposed cascaded controller, a control model is built in MATLAB/Simulink, and compared with whale optimization algorithm, particle swarm optimization, sparrow search algorithm to verify the response effect of the cascaded controller. A cascaded double PID controller is also built for comparison to verify the effectiveness of fractional order PID sliding mode controller. The results show that the cascaded fractional order PID sliding mode controller optimized by dung beetle optimizer is better than another cascaded double PID controller, produces the minimum torque ripple, and has better speed response and stability. The torque ripple of motor is suppressed. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm's performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity; while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This novel approach enhances the algorithm's adaptability by efficiently identifying moments when the optimization environment undergoes significant shifts. The detection of such changes is a crucial aspect of the PCSCM algorithm, contributing to its robust performance in dynamic scenarios. The effectiveness and robustness of the PCSCM algorithm are substantiated through extensive simulation experiments. These experiments provide insights into PCSCM's behavior in dynamic environments and showcase its ability to scale proficiently in high-dimensional settings. Particularly noteworthy are the results obtained when benchmarked against the Moving Peaks Benchmark and Generalized Moving Peaks Benchmark. These results not only underscore the algorithm's efficiency but also demonstrate its superiority when compared to several existing state-of-the-art optimization methods, including Multi-Swarm PSO, AmQSO, CPSO, Cellular PSO, FMSO, mQSO10 (5 + 5q), and DPSABC. [ABSTRACT FROM AUTHOR]

Natural fibre-reinforced hybrid polymer composites have gained significant attention worldwide in mechanical, aerospace, and automotive applications. Advanced machining techniques, such as abrasive water jet machining, have emerged as a solution to various challenges in this field, offering benefits such as the ability to shape complex geometries, achieve superior performance, improve surface characteristics, and attain high levels of accuracy. The research proposes a new approach for producing biodegradable hybrid composites composed of polylactic acid, bamboo particles, and montmorillonite clay using an innovative solvent-free stir-casting technique optimised for maximum efficiency. To systematically analyse the surface roughness, kerf angle, and material removal rate, a Box–Behnken design of experiments was employed, with the traverse rate, abrasive feed rate, and stand-off distance considered design variables. Analysis of variance was used to determine the significance of the differences between means of variables, while response surface methodology was utilised to establish the explicit relationship between the design variables and the response of the composite machining. The particle swarm optimisation algorithm was also employed to determine the optimal values of the design parameters for machining composites. The results showed that the traverse rate was the most influential factor, followed by the abrasive feed rate. In contrast, the stand-off distance had a relatively lower level of influence. The optimal process parameters were identified, resulting in a minimum surface roughness of 5.56 μm, a kerf taper of 0.0044 radians, and a material removal rate of 1175 g/min. [ABSTRACT FROM AUTHOR]

This study explores the mechanical properties of incompressible isotropic material polydimethylsiloxane (PDMS) using hyper-elastic constitutive models. It comprises two main parts: an experimental phase involving the creation of a new PDMS formulation and stress–strain evaluation through uniaxial tensile loading, and a theoretical phase where six hyper-elastic models are applied to the stress–strain data using finite element methods and optimization algorithms. Elastic compatibility and Drucker's stability criterion provide the determination of material constants, integrated into the generalized reduced gradient and constrained particle swarm optimization (C-PSO) algorithm for optimization. The performance of these models is assessed via the coefficient of determination. The Reduced Polynomial model, with six material parameters optimized through C-PSO, emerges as the top choice, closely matching experimental data at various strain levels. Subsequent finite element simulations validate the behavior of the Reduced Polynomial model under the same conditions as the tensile testing, showing excellent agreement with experimental results. Analyzing rubber-like materials and their composites using commercial finite element software is challenging due to their non-linear properties, motivating the use of optimization algorithms to determine material properties accurately. This research's novelty lies in using C-PSO and GRG solver to examine polymeric materials, yielding highly efficient and precise results. [ABSTRACT FROM AUTHOR]

The conventional manual design process and data acquisition for aerodynamic parameters can be time-consuming. Consequently, this study proposes a solution to address this issue. The main contributions of this study are as follows. (1) A surrogate model based on ensemble learning (Ensemble) is proposed, which utilizes a two-layer learner combining the Cokriging model (Cokriging) with the neural network model based on transfer learning (TL). A numerical example is conducted to evaluate its performance, comparing it with single-surrogate methods. (2) A criterion—the mp-cvvor hybrid sampling strategy—is proposed for application to any surrogate model. The efficiency of the mp-cvvor method compared to that of other sampling strategies is validated by a synthetic benchmark. (3) The aerodynamic optimization framework is applied to the RAE2822 airfoil. Our method reduces average computation fluid dynamics (CFD) calls by more than 48.2% than do the nondominated sorting genetic algorithm-II (NSGA-II) and particle swarm optimization (PSO), and the lift-drag ratio increases by 3.087% compared to the increase from the single-surrogate-based Cokriging approach. [ABSTRACT FROM AUTHOR]

Rebars are among the main materials used in reinforced concrete structures and among the costliest and most carbon-intensive construction materials. However, during the construction phase, rebar cutting schemes are usually not rationalized enough and may lead to up to 8% waste. Previous studies and practices showed that obtaining data on rebar cutting can be labor intensive and imprecise, lacking the profiling of quantity take-off factors. Additionally, existing optimization techniques often do not adequately consider field characteristics, such as the number of cutting patterns and the scale of the problem, and the characteristics of the cutting data. This study proposes a practical, integrated approach for minimizing rebar cutting waste during the construction phase. The proposed approach consists of two main parts. The first part is obtaining rebar cutting data based on building information modeling (BIM). In this paper, the main influencing factors of rebar quantity take-off are analyzed, and the modeling hierarchy is divided based on these factors to obtain accurate cutting data. The hierarchy also reflects repeatable commonalities between different rebar systems, which helps reduce repetitive modeling efforts and increase efficiency. In the second part, a cutting pattern-oriented multiobjective optimization method is proposed to optimize the cutting scheme. The process is divided into three stages: generating valid cutting patterns, optimizing their frequency, and combining a small number of remaining components. The method combines three algorithms—column generation, particle swarm optimization, and best-fit decreasing—to solve the problem according to the characteristics of each stage. The influence of multiobjectives on the final optimization result is further explored through a sensitivity analysis. The integrated approach was validated with test sets of different sizes, types, and complexities. Its effectiveness and generalizability in reducing rebar cutting waste contribute to its applicability and the achievement of construction sustainability goals. Practical Applications: Rebars are among the main materials used in reinforced concrete structures and among the costliest and most carbon-intensive construction materials. Due to the extensiveness of the construction management mode, rebar has inevitably become one of the main sources of construction waste. However, due to the lack of practical optimization methods, this percentage can reach 5%–8% or even higher. This study proposes a practical approach for minimizing rebar cutting waste during the construction phase. In this paper, the main influencing factors of rebar quantity take-off are analyzed, and the modeling hierarchy is divided based on these factors to obtain accurate cutting data. Then, the cutting patterns are optimized, including generating valid cutting patterns, optimizing the frequency of the cutting patterns, combining a small number of remaining components, and organizing them into cutting schemes. The integrated approach provides a flexible perspective for solving this problem. The approach was tested for its generalizability and adaptability in various construction scenarios using test sets of different sizes, types, and complexities. The effectiveness of the approach in reducing rebar cutting waste, combined with the consideration of field characteristics and engineering habits, contributes to its applicability and the achievement of construction sustainability goals. [ABSTRACT FROM AUTHOR]

Soils in the site are usually layered due to natural sedimentation, which causes nonuniformity along the depth. Conventional upper bound limit analysis is no longer suitable for analyzing such slope stability issues; hence, an alternative approach is required. To account for varying soil properties, including internal friction angle, cohesion, unit weight, and limit bonding strength at the soil–nail interface among different soil layers, the discretized technique was introduced to generate a potential failure mechanism. Considering the influence of soil parameters within the depth on the failure mechanism, three failure modes were employed to carry out the upper bound limit analysis of soil-nailed slope stability, including toe failure mode, intermediate failure mode, and bellow-toe failure mode. The upper bound solutions of the factor of safety and failure surface were determined by the strength reduction technique in combination with the particle swarm optimization algorithm. A finite-element limit analysis model of soil-nailed slopes in a layered ground by Optum G2 (version 2023 2.3.7) was established to verify the proposed upper bound limit analysis method. The results of the discretized technique were compared with those from the conventional upper bound analysis, the shear strength reduction finite-element method and limit equilibrium method in previous literature, and the finite-element limit analysis by Optum G2. To better estimate the influences of the soil nail inclination angle and soil thickness ratio H1/H on the factor of safety and critical failure surface, a parametric study was performed under two cases: a hard soil layer or a soft soil layer in the upper part of the ground. To evaluate the contribution of soil nails to slope stability, a dimensionless parameter η, meaning the ratio of the energy dissipation rate of soil nails to the total energy dissipation rate, was proposed and discussed in the parametric analysis. The results showed that the proposed discretized method agrees well with the shear strength reduction finite-element method, limit equilibrium method, and finite-element limit analysis (FELA). The simplified approach with weighted average parameters has a relative error greater than 10% and is not appropriate for stability analysis in the layered ground compared with the proposed discretized method and FELA. [ABSTRACT FROM AUTHOR]

Large deformation is a kind of geological hazard in the construction of soft rock tunnels, which hinders construction, threatens workers' safety, and raises project costs. Accurately obtaining the large deformation characteristics and patterns of tunnels is the prerequisite for taking targeted support measures. First, a viscoelastic–plastic model that can simulate both the short-term strain-softening effect and the long-term creep effect was proposed to investigate the large deformation features of soft rock tunnels. Then, a sophisticated model for creep parameter inversion was developed using the support vector machine, genetic algorithm, and particle swarm optimization. Finally, the deformation, stress, plastic zone, and internal forces in the lining of a large deformation tunnel were determined using the proposed constitutive model and the creep parameters obtained by inversion. As the creep displacement only makes up 14.4–23.2% of the total displacement, the results demonstrate that the elastic–plastic displacement is much more than the creep displacement. Notably, the connection between the top and middle benches has the most pronounced horizontal movement, accompanied by a significant strain-softening effect, which ultimately becomes the weak point in the support system. The excavation disturbs the surrounding rock, causing a high-stress zone and a low-stress zone, with the interface located around the junction of the elastic and plastic zones. The bending moment is positive at the wall waist and negative at the vault and arch waist. In addition, there is a progressive rise in the bending moment from negative to positive at the arch foot. [ABSTRACT FROM AUTHOR]

The numerical simulation of boundary value problems involving a high number of pile-type structures, such as pile foundation systems of high-rise buildings, represents a standard task in computational geotechnics. Due to their ability to simplify the underlying pile modeling process and decrease the simulation runtime embedded beam formulations (EBFs) have attracted widespread interest from the geotechnical community. State-of-the art EBFs are typically equipped with nonlinear interface constitutive models to capture the soil–structure interaction behavior with reasonable accuracy. However, reliable information concerning the calibration of related parameters is limited, which decreases the confidence in the results obtained with EBFs. The present work addresses this research aspect for the first time, along with a theoretical discussion of inherent simplifications in the numerical description of the soil–structure contact associated with EBFs. The significance of selecting adequate embedded interface constitutive model parameters to ensure EBF predictions with high credibility is demonstrated by means of parametric studies. This has motivated the development of an automatic calibration software, leveraged by particle swarm optimization, that provides guidance in selecting suitable values. The effectiveness of the calibration software is confirmed by back-calculation of different pile problems with default and calibrated parameter sets. Likewise, the results provide insight into crucial factors driving the calibration framework, with a view to increasing its potential for take-up in engineering practice. Potential lines of research in the context of the automatic calibration software are explored throughout this work and may serve as valuable reference in future research. Practical Applications: The design of geotechnical problems involving a high number of pile-type structures, such as pile foundation systems of high-rise buildings or wind turbines, is routinely assisted by finite-element analyses. The computational expense of these simulations is significantly influenced by the pile modeling technique. Due to their exceptional ability to simplify the underlying modeling process and reduce the runtime to an acceptable limit, embedded beam formulations have therefore attracted widespread interest for geotechnical design tasks. As with all soil–structure interaction problems, it is essential in embedded beam formulations to accurately describe the interface behavior between the soil and the structure. This work presents the first attempt to highlight the relevance of this concern and visually describes relevant limitations resulting from an inadequate selection of interface constitutive model parameters. This observation has motivated the development of an automatic calibration software to increase the confidence in the parameter selection. The high potential of this software to increase the credibility of numerical predictions obtained with embedded beam formulations is demonstrated based on pile problems with different soil stratification layers, pile geometries, and pile arrangements. In all cases considered, the results confirm that the numerical fidelity could be significantly increased by employing calibrated parameter sets. [ABSTRACT FROM AUTHOR]

The task scheduler belongs to the NP-complete problem, so it is very challenging in the cloud environment to develop one with reasonable performance and computation speed. Several studies take into account some important factors of the users or providers when addressing cloud task scheduling. This paper models cloud task scheduling as a Multi-objective Optimization Problem (MOP) that maximizes load balancing and execution times. Based on ring topology, we present a new multi-objective particle swarm optimization approach that utilizes intuitionistic fuzzy set to enhance evenness and diversity. The diversity and spread of the Pareto solution is adjusted using a Balanced Intuitionistic Fuzzy Crowding-Distance (BIF-CD) and Intuitionistic Fuzzy non-dominance sorting (IF-dominance). In order to identify the best compromise solution and in decision space and adjust evenness in objective space, ring topology is employed to identify a larger number of Pareto-optimal solutions. Experimental results for 25 benchmark multi-objective functions demonstrate the superiority of the proposed IF-MO-Ring-PSO over four state-of-the-art algorithms. We compare different quantitative measures to assess the uniformity and quality of Pareto fronts (PFs) found by our compared methods. The performance of the proposed method is evaluated on the benchmark test suites ZDT, DTLZ, CF, mDTLZ, and MMF, using the delta and space metrics and the progression metrics. According to the proposed method, ZDT test suite reduced space and delta metric by 13.36 and 15.11% respectively compared to other methods. The Wilcoxon's test and two-sample Mann Whitney's test are used to analyze the performance of proposed method on CF test suit. The analysis shows that the proposed method has better ability to generate quality PF with uniformity on constraint test suits. In comparison to four scheduling algorithms, the proposed scheduler also shows better performance according to load balancing, makespan, and resource utilization based on two datasets (i.e., HCSP and GoCJ). [ABSTRACT FROM AUTHOR]

The need for intelligence in today's era has tremendously increased the demand for Internet of Things (IoT) devices implanted to collect and process diverse data. Cloud computing offers a plethora of services to computationally constrained internet of things devices, but latency degrades the performance of real-time applications. A few other computing paradigms have been developed through the years to overcome this limitation of IoT devices. Fog computing, one of these paradigms, comes into picture as a backbone and offers services to applications which are required to be processed within a deadline. However, addressing challenges such as heterogeneity, offloading mechanisms, resource allocation, and complexity is crucial. This paper presents a framework for intelligent offloading mechanisms and an efficient resource allocation that results in improving the quality of service (QoS) parameters in an integrated cloud-fog-IoT environment. The proposed Fuzzy based Harris Hawks -Genetic Algorithm (HHGA) applies fuzzy-based logic to offload tasks to respective paradigms (cloud or fog), where the upcoming IoT request will be executed. In addition, the Fuzzy-based HHGA algorithm is developed by combining conventional Harris Hawks Optimization (HHO) and Genetic Algorithm (GA) to improve the exploration and exploitation. The proposed algorithm is eventually integrated with the present framework to search for the optimal resources for upcoming requests and reduce the service cost, time, and energy consumption. The experiments are conducted and consecutively the performance of the proposed framework is evaluated. The results demonstrate that the proposed algorithm outperforms Harris Hawks Optimization by 16.95%, Genetic Algorithm by 38.23% and Particle Swarm Optimization (PSO) by 23.09%. [ABSTRACT FROM AUTHOR]