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This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularly in search operators during the movement phase, as revealed through ablation experiments. EMBGO mitigates these limitations by integrating the movement and battle phases to simplify the original optimization framework and improve search efficiency. Besides, two efficient search operators: differential mutation and Lévy flight are introduced to increase the diversity of the population. To evaluate the performance of EMBGO comprehensively and fairly, numerical experiments are conducted on benchmark functions such as CEC2017, CEC2020, and CEC2022, as well as engineering problems. Twelve well-established MA approaches serve as competitor algorithms for comparison. Furthermore, we apply the proposed EMBGO to the complex adversarial robust neural architecture search (ARNAS) tasks and explore its robustness and scalability. The experimental results and statistical analyses confirm the efficiency and effectiveness of EMBGO across various optimization tasks. As a potential optimization technique, EMBGO holds promise for diverse applications in real-world problems and deep learning scenarios. The source code of EMBGO is made available in https://github.com/RuiZhong961230/EMBGO. • We quantitatively analyze the deficiency of the primary multiplayer battle game optimizer (MBGO). • We propose an efficient MBGO (EMBGO) by introducing novel differential mutation and Lévy flight. • Comprehensive numerical experiments on CEC2017, CEC2020, CEC2022, and eight engineering problems confirm the competitiveness of EMBGO. • We further apply the proposed EMBGO to solve adversarial robust neural architecture search (ARNAS) tasks. [ABSTRACT FROM AUTHOR]

The development of smart cities has been positively impacted by advances in Internet of Things (IoT) technology. In addition, new levels of service have emerged due to the demands of new types of applications, whereby these new service levels must be managed by priorities depending on the technological requirements of each service in order to efficiently route information from an origin IoT device to a base station. However, the current global energy crisis demands more awareness from technology systems in terms of energy consumption efficiency, reduction of carbon footprint, and sustainability. In this sense, we propose a mathematical optimization model capable of routing different services in an IoT network, considering different levels of priority in the services offered, while simultaneously reducing energy consumption in the network for services with priority. In other words, the proposal aims to extend the lifetime of IoT networks in critical energy urban infrastructure, ensuring the highest possible quality in the services offered on the network. Finally, our proposal is evaluated in different IoT network scenarios, considering different types of services and network sizes. [ABSTRACT FROM AUTHOR]

In the publishing industry, it is vital to comprehend customers' needs and take their pleasure into account. Thus, publishing houses (PHs) must effectively address the necessity to increase profitability as well as minimize the waiting time of the printing requests in the queue. Although some researchers have studied publishing industries' management, there is still a lack of research modeling PHs as a practical queueing system that reflects their performance precisely. Moreover, most models have not considered the machine learning technique for predicting the circulation of books integrated with a queueing model and mathematical programming. Furthermore, no study has modeled the PH as a M[X]/M/1 queueing system to practically improve the system. To fill these gaps, this paper develops a novel bi-objective mathematical model with a bulk arrival queueing system and a machine learning technique to minimize the total cost and maximize the resource utilization rate. Also, different machine learning classifier algorithms are implemented to estimate the circulation for each publishing product. Owing to the model's bi-objective character, an enhanced LP-metrics method is presented. We have also examined a real-world case study to validate our approach and demonstrate its applicability to real-world issues. A 25% increase in profitability served as proof of the model's effectiveness. [ABSTRACT FROM AUTHOR]

In this research, the focus is on addressing the capacitated vehicle routing problem (CVRP), a prominent logistical challenge within transportation management. The discrete salp swarm algorithm (DSSA) has been introduced as an innovative adaptation of the traditional salp swarm algorithm (SSA), specifically redesigned to cater to discrete characteristics inherent in problems like the CVRP. This algorithm integrates the core principles of SSA with mutation and crossover techniques, enhancing its applicability for discrete problems. The paper contributes in two main areas: firstly, the development of DSSA, tailored to address the unique requirements of VRP scenarios and ensuring a balanced approach between exploration and exploitation in discrete optimization. Secondly, the effectiveness of DSSA is demonstrated through practical applications, including an 8-customer routing task and a real-world case study involving cement delivery in Vietnam. In these scenarios, DSSA consistently demonstrates superior performance over other meta-heuristic strategies, marking a noteworthy advancement in the domain of optimization and providing a potential tool for complex routing challenges in logistics and distribution systems. [ABSTRACT FROM AUTHOR]

Deep hole boring vibrations pose challenges due to low stiffness of long cantilever bars, impacting accuracy and standards. Nonlinear modeling provides deeper insights into chatter. Integrating dynamic vibration absorbers enhances boring bar stability. In this study, a nonlinear model is developed to investigate the vibration stability of the boring process, with the model's dimensionless parameters analyzed and solved using the Runge-Kutta method. The impact of nonlinear parameters associated with the damping and stiffness of the absorbers on the vibration characteristics of boring bars is thoroughly examined. The findings reveal that there exists an optimal range for the damping and stiffness of the absorbers, where vibration amplitude is significantly reduced, especially under conditions of high excitation force. Moreover, it is demonstrated that the introduction of cubic stiffness elements can further diminish vibration amplitude. These results offer valuable guidance for the design and optimization of DVA in deep hole boring, contributing to improved machining performance and accuracy. [ABSTRACT FROM AUTHOR]

The established SDN multi-controller architecture faces the challenge of locating controllers to boost the scalability, reliability, and security of the network. Reliability is one of the important metric to handle when a failure event occurs in a network. In this paper, a Whale Optimization technique is used for controller placement and proposed Whale Optimization based Controller Placement (WOCP), that places the controllers in an optimized and efficient manner. The adequate placement of controllers minimizes the overall average latency with a high reliability index. The WOCP method resemblances with Minimizing Latency for Controller Placement (MLCP), resulting in an 18% reduction in latency in the Geant network topology. [ABSTRACT FROM AUTHOR]

This paper introduces a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) to tackle complex optimization problems. Although the original RIME algorithm is recognized as an efficient metaheuristic algorithm (MA), its reliance on a single, simplistic search operator poses limitations in maintaining population diversity and avoiding premature convergence. To address these challenges, we propose a hierarchical partition strategy that categorizes the population into superior, borderline, and inferior layers based on their fitness values. Individuals in the superior layer utilize an exploitative local search operator, individuals in the borderline layer inherit the expert-designed soft- and hard-rime search operators from the original RIME algorithm, and individuals in the inferior layer employ the explorative OBL method. We conduct comprehensive numerical experiments on the CEC2017 and CEC2022 benchmarks, six engineering problems, and extreme learning machine (ELM) training tasks to evaluate the performance of HRIME-MSP. Twelve popular and high-performance MA approaches are used as competitor algorithms. The experimental results and statistical analyses confirm the effectiveness and efficiency of HRIME-MSP across various optimization tasks. These findings practically support the scalability and applicability of HRIME-MSP as an advanced optimization technique for diverse real-world applications. • We propose a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) for solving complex optimization problems. • The proposed HRIME-MSP partitions the swarm population into the superior, the borderline, and the inferior layer. • Individuals in the different layers have distinctive search preferences. • Comprehensive experiments on CEC benchmarks, engineering problems, and extreme learning machine (ELM) training tasks are conducted. • The experimental results and statistical analyses confirm the efficiency and effectiveness of our proposed HRIME-MSP. [ABSTRACT FROM AUTHOR]

Embedded devices are used in many domains, including healthcare, industries, and home automation, all of which entail significant workloads. As a direct consequence, the embedded devices require retrieval and processing of data of large volume, which occupy large memory space in the embedded devices. Compression along with workload characterization is an effective technique for minimizing memory usage and to the improvement of endurance of memory in such devices. This paper investigates the embedded workload characterization using Extreme Learning Machine (ELM) that is particularly suitable for large-scale datasets and real-time applications. Though ELM is single-layer feedforward network, its input weight randomization has a considerable effect on the accuracy of the classification. In this paper, the authors have proposed a hybrid algorithm for the optimization of the randomization of ELM. The Particle Swarm Optimizer (PSO), the Genetic Algorithm (GA), the Ant Colony Optimizer (ACO), and the Whale Optimization Algorithm (WOA) were used in the optimization of the classification process in ELM to increase the accuracy of the results. The input data must be categorized based on the energy consumed by each workload to proceed with further processing according to the system requirements. This paper explores and analyses the performance of hybridized ELM-Genetic algorithm (ELM-GA), ELM-Particle Swarm Optimization (PSO), ELM-Ant Colony Optimizer (ELM-ACO), and ELM- Whale Optimization Algorithm (ELM-WOA) optimization algorithms. The embedded benchmarks Internet of Medical Things (IoMT), Mi Benchmark (MiBench), and Embedded Microprocessor Benchmark Consortium (EEMBC) have been used as a dataset for classification in this paper. The extensive experimental study shows that the hybridized ELM-WOA provides a 98.5 % classification accuracy, 98.1 % specificity, and 98 % sensitivity compared to the other optimization methods discussed in this paper. [ABSTRACT FROM AUTHOR]

Magnetic levitation systems are complex and nonlinear, requiring sophisticated control methods to maintain the stability and position of the levitated object. This research presents an optimized fractional order PID (FOPID) control approach for position control of a freely-suspended ferromagnetic object. The dynamic system model is mathematically modeled in MATLAB using first-principle modeling and the grey box method. The FOPID controller has five degrees of freedom (DOFs) that allow for fine-tuning of the control gains and fractional orders, enabling the system to handle the nonlinearity inherent in the magnetic levitation system. The DOFs of FOPID and integer order PID controllers are optimized using the Artificial Bee Colony (ABC) algorithm and results are compared with state-of-the-art optimization methods. The results showed that the FOPID controller can effectively control the magnetic levitation system with constraints and outperforms other methods by up to 92.14% in terms of settling time with negligible steady-state error. [ABSTRACT FROM AUTHOR]

With the rise in load demand, improving the voltage profile and reducing line loss is vital to ensure reliable power delivery to the customer. However, increasing power plant generation capacity is limited by environmental and economic factors, requiring a careful assessment of locally optimal options. Distributed Generation (DG) installation into the electricity grid is one of the reliable remedial actions to ensure a smooth power delivery. Installation of DG requires an optimization process to identify the appropriate placement and sizing. Inaccurate sizing and placement of the DG installation may result to over-compensation or under-compensation phenomena. This paper proposes a novel approach termed the Integrated Immune Moth Flame Evolutionary Programming technique (IIMFEP) for optimizing the installation of DG sources in distribution systems. It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. Two cases were considered in this study termed Case 1 and Case 2. Results in Case 1 discovered that the optimal sizing and placement of four Type III DGs exhibit the lowest power loss worth 2.74 kW (98.78 % reduction) for the 69-Bus RDS, while for the 118-Bus RDS the power loss is 319.89 kW (75.36 % reduction). In Case Study 2, the combination of DG Types I and III provided the highest power loss reduction. With one DG Type I and two DG Type III units installed, power loss was reduced by 97.15 % to 6.41 kW for the 69-Bus RDS and by 62.01 % to 493.21 kW for the 118-Bus RDS. The proposed IIMFEP managed to alleviate the setback experienced in the traditional EP, AIS and MFO which found to be stuck at local optimum. The IIMFEP method is compared to Moth Flame Optimization, Artificial Immune System and Evolutionary Programming and validated using the IEEE 69-Bus and 118-Bus Radial Distribution Systems, resulting in outstanding performance. [ABSTRACT FROM AUTHOR]

The interaction between a vehicle's tire and the road surface is pivotal for a driver's control over the vehicle's movements. It serves as the fundamental link between the vehicle and the road. The modeling of tires holds significant importance in contemporary vehicle design, playing a critical role in assessing aspects such as vehicle handling, ride comfort, and road load analysis. This study focuses on investigating the impact of the enveloping behavior characteristics of a pneumatic tire on the performance of a suspension system. The analysis of the vehicle's ride comfort utilizes a half-car model. Unlike a previous model with a single point of contact with the road, the presented suspension system, coupled with a four-degree-of-freedom rigid ring tire model, offers a more precise estimation of both ride comfort and road holding. The primary emphasis of this research lies in the modeling and evaluation of the proposed suspension system's performance. A comprehensive computer model of the entire system is developed using MATLAB software. This work enhances the existing framework by incorporating both a Multi-Objective Genetic Algorithm (MOGA) and a Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the damping and stiffness coefficients of the passive suspension. This approach allows for a detailed comparison of the optimization capabilities and effectiveness of both algorithms in refining vehicle ride comfort. The results from MATLAB simulations highlight performance improvements, and the comparative analysis of MOGA and MOPSO provides insights into the selection of optimization techniques for suspension system design. [ABSTRACT FROM AUTHOR]

Feature subset selection is considered as the most essential pre-processing step. Metaheuristic approaches may be employed to discover a solution to difficulties in feature selection, which can be viewed as an optimisation problem. The aim of the system is to provide a hybrid binary metaheuristic algorithm that combines gorilla troop optimisation and genetic algorithm to handle the feature selection issue effectively. This new method is called GTO-GA. To ensure that the optimisation technique converges fast and properly and to enhance the exploration process, the GA were used. The suggested technique is tested for stability and robustness using 16 medical datasets taken from the Kaggle and UCI repositories. To evaluate the chosen features' performance in classification issues further. The results show that the algorithm outperforms 10 top-tier optimisation methods, including PSO, ALO, the original GTO and the SCA algorithm. The results highlighted the statistical difference, superiority and importance of the suggested feature selection strategies. [ABSTRACT FROM AUTHOR]

Facility Layout optimization is a crucial aspect of operations management, impacting efficiency, productivity, and overall operational costs. In dynamic environments, where layout adjustments are frequent due to changes in product demand, machinery breakdowns, or workforce variations, it becomes imperative to employ efficient optimization techniques. This research paper presents an investigation into the performance of various Genetic Algorithm (GA) variants to address the challenging Dynamic Facility Layout Problem. Three primary approaches for dynamic facility layout optimization have been examined: Genetic Algorithm (GA), GA with Local Search, and Machine Learning-Enhanced GA for robust layout design. The results show that the Machine Learning-Enhanced GA outperforms the traditional GA and Genetic Algorithm with Local Search in terms of both solution quality and adaptability to dynamic changes. This suggests that leveraging machine learning techniques can significantly enhance the effectiveness of Genetic Algorithms in addressing DFLP. [ABSTRACT FROM AUTHOR]

To address the parameter instability issues in hazardous materials handling during multi-machine loading and unloading operations, we propose a Full-Scale Smart Parameter Optimization Control (FSPOC) system specifically designed for multi-machine coordination. This system leverages a novel fish scale prediction algorithm tailored for cooperative multi-machine environments. Initially, the fish scale prediction algorithm, inspired by bionic fish scales, is developed to predict future system behavior by analyzing historical data. Building on this algorithm, we introduce a disturbance cancellation control theorem and design a parameter optimization controller to enhance stability in high-dimensional nonlinear spaces. The FSPOC method is then applied to a multi-machine cooperative system, enabling online distributed parameter optimization for complex systems with multiple degrees of freedom. The effectiveness of the proposed method was validated through simulations, where it was compared with two other optimization techniques: Genetic Algorithm-based PID (GAPID) and Chaotic Atomic Search Algorithm-based PID (CHASO). The simulation results confirm the superiority of the FSPOC method. [ABSTRACT FROM AUTHOR]

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO's applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains. [ABSTRACT FROM AUTHOR]

Socio-natural disasters pose a significant risk to healthcare systems, particularly in areas with high volcanic and seismic activity, combined with high poverty rates, reflecting vulnerable communities facing severe impacts on societal well-being and the economy. This is the case in the territories of Coquimbo and La Serena in Chile, which are constantly affected by natural phenomena, such as wildfires. Between 2020 and 2021, a total of 58 wildfires were recorded. This study presents a quantitative risk analysis of the healthcare network in response to the recurrence of wildfires in Coquimbo and La Serena cities, employing mixed-integer programming models for optimal location, relating fixed costs to the increased demand for care from the affected population. The methodology applied was quantitative, where the first phase involved identifying the areas affected by wildfires in La Serena and Coquimbo. Subsequently, we have formulated the mathematical model, including the objective function, definition of variables and parameters, definition of constraints, and finally, the implementation of the mathematical model's code. [ABSTRACT FROM AUTHOR]

Artificial Potential Field techniques (APF) have become a popular path planning technique due to their simplicity and fast-execution nature. APF techniques have been widely known for their outstanding performance against the path quality and computational time trade-of problem for path planning techniques. However, Since APF techniques are optimization problems, they suffer from well-known problems such as the minima trap problem and the narrow passage problem. The APF planner can fail to find an obstacle-firee path to the final goal point and simply be trapped in a local minima for some map configurations. This paper proposes a new path planning technique that aims to take the advantage of the APF technique as well as solving the APF problems of local trap and narrow passage problems. The proposed technique is inspired by the fluid dynamic concepts and adopts an artificial "flushing" operation. The robot and the obstacles will be placed in an artificial pool. The robot will have negligible weight while the obstacles will be fixed in the pool. Once the "flushing" operation starts, the robot will be automatically pulled by the water to the flushing point which is the goal point. It is guaranteed that the water in the whole map will be absorbed into the flushing point and no particles will be trapped in any region. The simulation results have proven the effectiveness of the proposed technique and demonstrated its ability to overcome the challenge of local minima traps in potential field path planning. [ABSTRACT FROM AUTHOR]

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway's VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC. [ABSTRACT FROM AUTHOR]

Controlling the unstable processes is challenging since one or more poles are located on the right side of the s-plane. The existence of dead time in these systems makes control much more difficult. In this paper, internal model control based sliding mode control has been proposed for the control of unstable processes with dead time. Two sliding surfaces based on PID and PIDPI are used to design of the proposed controller. The parameters of continuous and discontinuous control law are obtained using differential evolution optimization technique. An objective function is constituted in terms of performance measure (integral absolute error) and control effort measure (total variation of controller output). Illustrative examples demonstrate the superiority of the proposed controller over earlier reported work in this realm, especially in terms of load disturbance rejection. A case study on temperature management of a continuous stirred tank reactor during an irreversible exothermic process also serves to highlight the applicability of the proposed system. Furthermore, robustness of the proposed controller is also investigated by inclusion of perturbations in the parameters. The obtained results clearly show how well the suggested controller works. [ABSTRACT FROM AUTHOR]

The aim is to assess the impact of the civil sector's heat demand electrification on the entire energy system, in the Italian case study. The hourly heat demand profiles are estimated at census cells level using the BIN method or monitoring data. Profiles are used as input in the oemof-based NEMeSI model employed to optimize both the capacity expansion of power generation and the operation of the power system in 2030, with hourly temporal resolution and NUTS2 spatial detail, in three heat demand electrification scenarios. The model considers the availability of sources, the import and export profiles, and the limit on renewable sources. The results show that no additional capacity of renewable energy is driven by the increasing electrification in fact the installed capacity remains the same in the three scenarios (97GW of photovoltaic and 33GW of wind turbines). The increase of power demand results in a reduction of overgeneration (48.6TWh to 38.3TWh), an increase in installed batteries (40.6GWh to 115.3GWh) and in CHP (+10%) and CCPP systems (+16%). The results show a slight increase of natural gas in electricity generation (+5.2TWh) respect to a high reduction in its use in civil sector's heat demand (-68.5TWh). [ABSTRACT FROM AUTHOR]

This paper addresses the control of a nonlinear system affected by deadzone effects, using a constrained actuator. The system itself incorporates a second-order oscillatory dynamic actuator, with an unknown nonlinear input-output relationship. The proposed algorithm not only accommodates the deadzone constraints on control inputs but also considers the actuator's saturation limits in control input calculations. It introduces a trajectory tracking mechanism that, instead of directly following the primary trajectory, adheres to an alternative trajectory capable of stable tracking, gradually converging to the main trajectory while accounting for operational constraints. In practical control systems, the actuator's input-output relationship is often nonlinear and unknown, requiring inversion for model-based control. This paper employs an offline-trained neural network trained on synthetic data to identify and approximate the actuator's behavior. To optimize the control system's performance and ensure stability during sudden error changes, the control input operates in two modes: position and velocity control. This dual-mode control allows for continuous switching between the two, facilitated by an innovative optimization technique based on the gradient descent method with a variable step size. Simulation results validate the effectiveness of the proposed algorithm in controlling systems constrained by hard limits and featuring nonlinear oscillatory actuators, providing a valuable contribution to the field of control systems. • Developing a dual mode non-predictive control algorithm based on discrete-time system equations. • Utilizing an offline-trained neural network for estimating the inverse of the nonlinear actuator relationship. • Stable desired trajectory mapping based on inherent and actuation constraints while maintaining asymptotic stability. • Substituting the trajectory within a fixed range outside of the constant deadzone and inside the variable saturation. • Optimized balancing position and velocity control modes through a weighted average of two control forces. [ABSTRACT FROM AUTHOR]

The optimal control design of the boiler-turbine system is vital to ensure feasibility and high responsiveness over desired load variations. Using the traditional linear control techniques realization of this task is difficult, as the boiler-turbine mechanism has strong nonlinearities. Besides, environmental and economic concerns have replaced existing tracking control ones as the primary concerns of advanced power plants. Thus, this study proposes an optimal economic model predictive controller (EMPC) scheme for this unit on the basis of the input/output feedback linearization (IOFL) method. By employing the IOFL method, this unit is decoupled into a new linearized model that is utilized for developing the suggested optimal IOFL EMPC technique. The proposed control scheme is formulated in an economic quadratic programming form that considers the input-rate and input limits of the unit for optimal economic performance. In addition, an adaptive iterative algorithm is utilized for constraints mapping with guaranteeing a feasible solution in a finite number of steps without violation of original constraints over the entire predictive horizon. The outcomes of the simulation show that the suggested optimal IOFL EMPC scheme offers an improved dynamic and economic output performance over fuzzy hierarchical MPC, fuzzy EMPC, and nonlinear EMPC techniques during various load variations. • An optimal EMPC scheme based on IOFL method is proposed for boiler-turbine unit. • The IOFL EMPC scheme is formulated in an economic QP form including constraints. • An adaptive iterative algorithm ensures feasible solution without limits violation. • The IOFL EMPC scheme enhances dynamic economic performance for boiler-turbine unit. [ABSTRACT FROM AUTHOR]

The cloud computing is the new technique used most today for computation and storage. Edge computing is also a new computing domain that gives computation of tasks and storage of data nearer to the data sources. Various applications of edge computing are IOT, healthcare, retail, manufacturing etc. Virtualization technologies have enabled cloud platforms to provide various services such as virtual machines to the users. Task scheduling is the major issues faced in both cloud and edge domains. Since the usage of machines is high, it is difficult to assign tasks manually. So, an efficient and optimized algorithm is required in the cloud as well as the edge environment. Here, a task scheduling is implemented based on Heuristic Optimization Technique to examine the performance measures using cloudsim. Task scheduling and optimization techniques dynamically allocate resources in the cloud environment. Similarly, in edge computing, scheduling the task is performed with different optimization techniques such as random fit, and max fit algorithms. These task scheduling methods are allocated to the VM based on the resources that are available at the VM. These algorithms are implemented, and it increased the VM utilization by 4.82%. The processing time taken is reduced by 2.67% and the percentage of failed tasks is reduced by 3.68%. The experiment result shows the importance of the scheduling in the cloud and edge environment, in terms of processing time, failed task and increase in VM utilization in edge devices. [ABSTRACT FROM AUTHOR]

Contour error is a crucial quality measure for stone relief products. Achieving the appropriate process parameters is vital due to the inherent hardness, brittleness, and natural crystal defects found in white marble. This study investigated various processing characteristics of Chinese character stone relief and primarily focused on understanding the impact of spindle speed, feed speed, milling width, and milling depth on contour error. The study optimized the process parameters by considering the contour error as the response target. Firstly, the paper outlined the machining process of stone relief using a manipulator and provided a classification basis for different machining features. Subsequently, a series of white marble milling experiments were conducted using the Box-Behnken design method of Response Surface Methodology (RSM). A multiple nonlinear regression model was established to analyze the influence of various processing characteristics on white marble contour error. Through RSM, the interaction effects of different factors on the contour error were examined. Finally, optimal milling parameters were selected and validated through experiments. The results indicated that the optimized milling process proposed in this study can reduce the contour error of the final stone relief products by 47.3% compared to traditional milling methods. [ABSTRACT FROM AUTHOR]

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With the rapid development of studio informatization, the traditional era of studio construction is gradually entering digitalization. In recent years, the rapid development of cloud computing technology in the field of education has greatly changed the way teachers teach and students learn. The main goal of the digital studio cloud platform is to improve the teaching efficiency and quality of teachers, and provide students with a more convenient platform. At present, education cloud platforms are still in the early stages of development, and many designs focus on functionality while ignoring the core needs of users, leading to difficulties in user operation and hindering the comprehensive promotion of education cloud platforms. This article proposed a digital studio construction strategy based on cloud computing, using systematic and intelligent methods to improve the traditional studio teaching mode, strengthen objective evaluation and data guidance functions, and explore the standardized and systematic teaching mode of studios. This article constructed the ZStack private cloud platform to achieve flexible resource allocation and efficient utilization, and evaluated performance under different workloads through experiments, providing important reference for the optimization of future education cloud platforms. [ABSTRACT FROM AUTHOR]

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Seven-level, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements. [ABSTRACT FROM AUTHOR]

An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task does not go away. In this study, Salt and Pepper Denoising, Hybrid Balancing Composite motion Optimization with Adaptive Switching Modified Decision based Unsymmetric Trimmed Median Filter and Forensics-Based Investigation Algorithm is proposed (R-SPN-ASMD-UTMF-Hyb-BCO-FBIA). Initially, the input images are obtained from boat-types-recognition dataset, cat-breeds-dataset, cars-image-dataset, butterfly-images 40-species dataset and birds-200 dataset. The images are pre-processed through an ASMD-UTMF filter. ASMD-UTMF does not expose any adoption of optimization systems to calculate the optimal parameters. Therefore, the proposed Hyb-BCO-FBIA is employed for optimizing the ASMD-UTMF weight parameters. The suggested system is implemented on MATLAB and the assessment metrics as Mean Square Error (MSE), Structural similarity index measurement (SSIM), Peak signal to noise ratio (PSNR), Normalized cross-correlation (NC), Image Enhancement Factor (IEF), Mean Square Error (MSE) are analysed. The proposed method attains higher PSNR, NC related with other SOTA (State-Of-The Art) methods. [ABSTRACT FROM AUTHOR]

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system's Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system's robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches. [ABSTRACT FROM AUTHOR]

In machine learning, hyper-parameter optimization (HPO) aims to tune the set of parameters that controls the learning process. HPO could be time-consuming and resource-intensive due to the huge parameter search space and the complexity of models such as deep neural networks. Many of the existing HPO techniques tend to be variants of Bayesian optimization methods; each of which has been applied successfully for model tuning in different application domains. However, these Bayesian optimization methods have not been systematically evaluated against each other in the context of deep learning based malware detection. In this paper, we report a large-scale empirical study comparing popular HPO techniques on the performance of deep learning based malware classifiers. We use a diverse collection of seven datasets covering the most typical features used in malware detection. We conduct our experiments with Ray Tune, a distributed tuning platform, and popular optimization libraries such as Optuna, HyperOpt, Nevergrad, etc., across a wide range of computing platforms including AWS EC2, high-performance workstation, and laptop computers. Our extensive experiments provide useful insights into the application of different HPO techniques in deep learning based malware detection. [ABSTRACT FROM AUTHOR]

This paper presents a decision support system tailored for a nut and dried fruit company located in the Canary Islands, Spain. Its primary objective is to enhance production planning by harnessing advanced optimization methods. The inception of the system is driven by the critical need to boost efficiency, reduce costs, and quickly adjust to the constantly shifting needs of the market demands and supply chain variables. By incorporating optimization techniques directly into the system, it tackles the complexities of making real-time, informed decisions within a challenging, constrained production landscape. The computational experiments demonstrate the optimization technique's remarkable efficiency in addressing both synthetic and real-world scenarios. Furthermore, the system boasts a user-friendly web interface for visualizing results, empowering managers to make well-informed production planning decisions. [ABSTRACT FROM AUTHOR]

The study attempts to assess the decision-making abilities of the knowledge-based system model based on classic fuzzy Petri nets and their subtypes depending on the mode of their operation. In addition, the relationship between the quantity and quality of the proposed decisions was investigated using additional knowledge of the structural and dynamic properties of the net used in the model. The change in the dynamics of the net model was influenced by the appropriate selection of the admissible triples of operators assigned to net transitions in accordance with the knowledge about the partial order between the triples. The theoretical considerations were supported by the analysis and evaluation of the results of the experiments carried out on the net model of the exemplary knowledge-based system for train traffic control. According to the authors, this work fits into the list of challenges formulated in the review article on the state of research on the theory and applications of fuzzy Petri nets, prepared by authors Zhou and Zain and published in Artificial Intelligence Review, 45, 405–446, 2016. [ABSTRACT FROM AUTHOR]

This study focuses on the issue of assigning hospital beds to patients based on their medical condition within a specific length of stay. To handle this problem a two-stage approach combines machine learning (ML) techniques and optimization is proposed. The first level seeks to classify patients into three categories based on their contagious states by considering various features. The second one aims to minimize the cost of assigning patients to specific beds according to their states under a set of constraints (capacity constraints, transfer constraints, etc). The problem is formulated as a linear programming to increase the patient admission rate during emergencies. The proposed methodology is tested using a set of instances based on real data taken from Tunisian Hospitals. Experimental results illustrate the effectiveness of this approach, the best model is chosen based on the highest accuracy, specificity, and sensitivity. [ABSTRACT FROM AUTHOR]

Creation of a schedule becomes an important and complicated matter for university. Well optimized schedule allows to spare time and money and even helps increasing the learning effectivity. The suggested solution of schedule creation tool is mainly based on incorporation of multiagent system and simulated annealing meta-heuristic. The multiagent system is a parallel distributed system based on activity of multiple autonomic units named agents. As it is known the usage of great number of very simple agents working in parallel can provide interesting and valuable result. The tested algorithm has been initially implemented for the schedule creation program within the own university where it works properly helping to arrange the plan of students in several fields of study. It is planned in the nearest future that basic schedules will be optimized by more advanced AI technologies and spread out to other universities. [ABSTRACT FROM AUTHOR]

For improving the energy efficacy and control performance, integration of swarm optimization with controller design could successfully reach this objective. In this study, a comparative analysis has been conducted between two decentralized control structures based on optimized Proportional-Integral-Derivative (PID) and PID-Proportional (PID-P) controllers for optimal controlling of heating system in multi-zone building. Based on the energy balance equation, the mathematical dynamics model of the heating system is established in the building. In order to enhance and optimize the performances of both controllers, their design parameters are tuned based on Whale Optimization Algorithm (WOA). Two objectives have been considered in the optimization process of heating system. The first objective is to minimize the error in temperature, between the desired and real temperatures, based on IAE (Integral of Absolute Error) index, while the second objective is the minimization of the heat energy consumption. The normalization method has been used to adjust between the two differently-scaled objectives. Simulation results based on MATLAB reveal that the PID-P controller achieved better performance in terms of providing comfort indoor temperature with energy savings as compared to the PID controller. [ABSTRACT FROM AUTHOR]

Optimization theory in finance is developing rapidly, especially in forming portfolio optimization by including fuzzy elements. However, some fuzzy methods encountered problems in their application when obtaining optimal portfolio weights. Hence, a multi-objective approach was required to solve the fuzzy portfolio. This research focused on compiling a fuzzy portfolio using large-scale data without considering short-selling. Then, the data consisting of opening, closing, highest, and lowest prices were modeled into non-linear adaptive fuzzy numbers. The incoming fuzzy number was a trapezoidal fuzzy number. The data was obtained from 491 trading days of the ten most active stocks in LQ45, respectively. This research also employed the Treynor ratio (TR) to optimize the process. As a control for the Treynor ratio, the Sharpe ratio (SR), which had been employed, was also first introduced. Based on empirical data, there were differences in the composition of the weights resulting from the formed fuzzy portfolio. The research results significantly indicated that TR had a more even diversification level across all stocks when constructing a fuzzy portfolio with a multi-objective approach. It indeed reduced the systematic risk in the portfolio formed. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) algorithm is optimization technique that works well on complex optimization problems, but it's potential is constrained by the shortcomings that insufficient local search and slow convergence. To alleviate these challenges, an improved ABC variant with observed subgroups (OSABC) is proposed. In this study, each food source has an observed subgroup that is determined by calculating its Euclidean distance from the other. And the subgroups' size adaptively changes according to the ranking. Then, the new update equation is constructed by the food source from the subgroup. Additionally, to mitigate the scenario in which ABC faces strong selection pressure later on, we integrate a ranking-based selection mechanism with the fitness-based selection probability to design a dynamically adjusted selection probability. The numerical experimental results of OSABC with excellent ABC variants on optimization problems and their shifted versions show that OSABC has better solution accuracy and faster convergence rate. Meanwhile, OSABC's practical applicability has verified on the wireless sensor network (WSN) coverage optimization problem. [ABSTRACT FROM AUTHOR]

This study investigates the impact of pulse-on-time (T ON), current (I), pulse-off-time (T OFF), voltage (V), and tool electrode speed (TES) on material removal rate (MRR), average surface roughness (R a), power consumption (PC) and surface defects for electrical discharge drilling (EDD) of Waspaloy. Based on a Taguchi design with five factors and four levels, experimental trials reveal T OFF , T ON , and T OFF as the most influential factors, contributing 50.45%, 34.29%, and 33.09% to MRR, R a , and PC , respectively. To address conflicting conditions and optimize responses, non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective genetic algorithm (MOGA), multi-objective grey wolf optimizer (MOGWO), and multi-objective particle swarm optimization (MOPSO) are employed. Comparative analysis of 8 optimal solutions provided by four optimization techniques, which exhibit less than 10% errors in confirmatory experiments, has been done, ensuring better prediction accuracy. MOPSO is preferable to the rest of the optimization techniques due to its lower percentage error. NSGA-II, MOGA, MOGWO, and MOPSO exhibit relative improvement in responses based on confirmatory experiments, establishing them as effective tools for exploring EDD applications. Higher current is confirmed as a primary contributor to increased surface damage through microscopic image analysis, and EDX analysis exposes carbon deposition migration on both the tool and workpiece. [ABSTRACT FROM AUTHOR]

In the field of neural language processing, current research projects that apply meta-heuristic approaches to Arabic text are extremely limited given the complexity of this language in terms of structure, grammatical rules, morphology, syntactic analysis and derivation rules. In this paper, we propose a new approach for processing Arabic documents using the chaotic grey wolf optimization technique, which is employed as a supervised learning method by simulating the behavior of grey wolves in searching, circling and pursuing their prey. The proposed method is divided into four phases. The term weighting phase uses TF- IDFC-RF, which is based on TF-IDF and the relevance frequency of terms in documents and classes. The feature selection phase combines the chaotic method of the grey wolf optimization algorithm to both reduce and identify relevant features for model building. In the third phase, we use three classifiers: firefly algorithm, SVM and NB to classify documents into several classes. In the experimental process, we used two collections of reference documents to present a comparative study of the different term weighting systems. The experimental results show that the new architecture, based on the chaotic optimization of the grey wolf algorithm with the Firefly algorithm, obtained the best results in terms of accuracy. [ABSTRACT FROM AUTHOR]

Grinding is one of the most widely employed machining processes in manufacturing. Achieving a successful grinding process characterized by low fault rates and short cycle times can significantly improve overall productivity and process efficiency. Nonetheless, finding optimal values for grinding process parameters is challenging due to complex underlying dynamics. Therefore, this work proposes a data-driven system that exploits various machine learning techniques and metaheuristic optimization algorithms to optimize grinding process parameters. Using data collected from grinding processes, the proposed system constructs a machine learning-based fault detection model and employs that model to define variable range constraints. In addition, a Gaussian process-based cycle time estimation model is developed. Process parameter optimization is performed using various metaheuristic algorithms based on the aforementioned methods. Experiments with actual internal cylindrical grinding process data have proven the proposed system's effectiveness during process parameter optimization. Furthermore, real-world validation data verifies the final optimization solution, reducing the fault rate and process cycle time by 77.83% and 17.64%, respectively. In-depth interviews with six domain experts in the grinding process also verify the proposed system's validity and real-world applicability. The proposed data-driven system is expected to bring substantial improvements in process productivity, especially when applied to manufacturing sites in practice. [ABSTRACT FROM AUTHOR]

Machine vision is based on computer, through the comprehensive utilization of a series of advanced theoretical knowledge such as image acquisition equipment, digital processing technology and related algorithms. It can quickly and accurately extract the required features from the input data and describe them, which plays an important role in modern industrial production. Machine vision can intuitively express the image information that people are exposed to in a certain way, and process and process it accurately and quickly. This paper aims to use machine vision to quickly obtain spatial position, size and other data, combined with artificial intelligence genetic algorithm, particle swarm algorithm, intelligent plane optimization. This paper mainly uses the experimental simulation method and the comparison method to study the optimization method of the system layout, puts forward two improvement schemes of the storage system layout, and makes a comparative analysis of their space loss parameters. The experimental results show that in terms of total space loss, the value of improved scheme 1 is 0.741, which is higher than that of improved scheme 2. [ABSTRACT FROM AUTHOR]

The prosperity of e-commerce has made our lives more convenient, but the logistics behind it has been silently paying. The e-commerce industry is expanding at an unprecedented rate, and with it comes explosive growth in logistics demand, especially in the warehouse distribution link. In order to meet this demand, the warehouse distribution system needs to be improved both in terms of technology and efficiency. This rapid development also brings a series of problems. First of all is the problem of logistics congestion. Due to the surge in orders, especially during peak shopping periods, logistics centers are often under great pressure. This not only affects the speed of delivery, but can also lead to damage or loss of goods. The purpose of this paper is to find an effective method to optimize the logistics warehouse allocation system of e-commerce, and to provide an efficient and reliable logistics optimization scheme for e-commerce enterprises. In this paper, data analysis and simulation methods are used to analyze. The experimental results show that PSO has the largest improvement in the order clerk operation efficiency (13%) and the smallest improvement in the order status (1%). [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the traditional information management system faces the challenge of efficiency and accuracy when dealing with complex data and decision-making problems. In order to solve these problems, this paper integrates the random leapfrog band selection algorithm into the information management system. This algorithm shows excellent performance in dealing with large-scale, nonlinear and multi-variable optimization problems, resource allocation, scheduling optimization and data mining. This paper mainly explains the design framework of information management system, the optimization of information management by random leap-band selection algorithm and the design of information management system. Finally, two groups of simulation experiment results are as follows: the accuracy of the optimized information management system is 96%, the running time is 7.1min, the algorithm performance is better than the traditional system, and the response time of the system is reduced by 195ms. This research provides an efficient, flexible and scalable solution for dealing with various complex information management system optimization problems. [ABSTRACT FROM AUTHOR]

We propose a number‐theory‐based quantized mathematical optimization scheme for various NP‐hard and similar problems. Conventional global optimization schemes, such as simulated and quantum annealing, assume stochastic properties that require multiple attempts. Although our quantization‐based optimization proposal also depends on stochastic features (i.e., the white‐noise hypothesis), it provides a more reliable optimization performance. Our numerical analysis equates quantization‐based optimization to quantum annealing, and its quantization property effectively provides global optimization by decreasing the measure of the level sets associated with the objective function. Consequently, the proposed combinatorial optimization method allows the removal of the acceptance probability used in conventional heuristic algorithms to provide a more effective optimization. Numerical experiments show that the proposed algorithm determines the global optimum in less operational time than conventional schemes. [ABSTRACT FROM AUTHOR]

Electrical Impedance Tomography (EIT) reconstructs the electrical properties of cellular tissues by taking measurements at their boundaries. Its non-invasive nature, safety, and potential for an extensive variety of therapeutic uses have sparked significant interest. The generation of EIT images involves solving an inverse problem through iterative optimization techniques. As a result, the effectiveness of various optimization strategies for EIT may vary. This study evaluates the efficacy of the Adam optimizer in addressing the EIT inverse problem. Our simulations reveal that Adam demonstrates superior convergence rates, faster reconstruction times, and higher-quality images compared to traditional gradient descentbased optimizers. Specifically, the Adam optimizer produces images of superior quality with improved anomaly localization, all while achieving faster reconstruction speeds and higher convergence rates. Additionally, we provide insights into the optimal parameter configurations for Adam in the context of EIT, offering valuable guidance for future research endeavors in this domain. All things considered, our findings unequivocally demonstrate that the Adam optimizer is a valuable tactic for resolving the EIT inverse problem. [ABSTRACT FROM AUTHOR]

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This paper presents an innovative approach to optimize traffic networks in a supplier-customer system based on specific strategies of game theory. The traffic network is represented as a routing configuration in which the arcs represent various possible roads between supplier and customer entities, each route having an associated transport cost for the product unit. The game structure is defined by a matrix of transport costs between the players, supplier and customer, with their corresponding strategies. Based on these costs, the game matrix is built to which the existing quantities in warehouses are added, respectively the quantities requested by the final customers. By solving the game associated to the transportation traffic network, the different strategies are proposed for choosing the optimal solution. The simulation results confirm the effectiveness of the proposed approach and recommend some performant procedures for solving the game, such as Hurwicz, Laplace and Savage, close to optimal solution. This research opens new directions for management optimization in other domains such as comunication, electrical and administrativ networks. [ABSTRACT FROM AUTHOR]

In this paper we present our Lua programming language libraries that enable modeling and solving mixed integer linear and nonlinear mathematical optimization problems. On one side, these libraries provide a framework for developing algorithms written in Lua that use certain well known solvers within more complex procedures. On the other side, they facilitate the transformation of both input and output data of a mathematical programming problem, in order to compute the standardized coefficients that have to be transmitted to the solvers or prepare solution of solved problems for further data processing. Both of these use cases, together with Lua programming language simplicity, versatility and performance, make Lua a suitable programming language for use in scientific and engineering researches. [ABSTRACT FROM AUTHOR]

This work presents a resilient distributed optimization algorithm based on the event-triggering mechanism for cyber–physical systems (CPSs) to optimize an average of convex cost functions corresponding to multiple agents under adversarial environments. Two attack scenarios, including the f -total (each agent is affected by at most f malicious agents in the whole network) and the f -local (each agent is affected by at most f malicious agents in its in-neighbor set) attacks are considered. Subsequently, the convergence conditions under these two attack scenarios are provided, respectively, both of which guarantee that the state values of benign agents converge to a bounded error range. The optimality conditions are also presented by theoretical analysis, which guarantee that the state values of benign agents converge to a safety interval constructed by local optimal values under certain graph conditions, despite the misbehavior of malicious agents. In addition, four numerical examples are presented to show the effectiveness and superiority of the event-triggering resilient distributed optimization (RDO-E) algorithm. Compared to existing resilient algorithms, the proposed method achieves resilient distributed optimization with higher accuracy and less demanding communication overheads. Finally, by applying the proposed method to the multi-microgrid system, a resilient economic dispatch problem (REDP) is successfully solved, which validates the practical viability of the RDO-E algorithm. • An event-based resilient distributed optimization (RDO-E) algorithm is developed. • The convergence and optimality analysis are conducted under different attack scenarios • The RDO-E algorithm outperforms existing algorithms in accuracy and communication resource saving. • The RDO-E algorithm is applicable to practical muli-microgrid systems. [ABSTRACT FROM AUTHOR]