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A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

Microgrid has been extensively applied in the modern power system as a supplementary mode for the distributed energy resources. The microgrid with wind energy is usually vulnerable to the intermittence and uncertainty of the wind energy. To increase the robustness of the microgrid, the energy storage system (ESS) is necessary to compensate the power imbalance between the power supply and the load. To further maximize the economic efficiency of the system, the system level control for the microgrid is desired to be optimized when it is integrated with the utility grid. Aiming at the aforementioned problem, this paper comprehensively analyzes the power flow of a typical loop microgrid. A transformer-based wind power prediction (WPP) algorithm is proposed and compared with recurrent neural networks algorithm. With the historical weather data, it can accurately predict the 24 h average wind energy. Based on the predicted wind energy and the time-of-use (TOU) electricity price, a day-ahead daily cycling profile of the ESS with particle swarm optimization algorithm is introduced. It comprehensively considers the system capacity constraints and the battery degree of health. The functionality of the proposed energy management strategy is validated from three levels. First, WPP is conducted with the proposed algorithm and the true historical weather data. It has validated the accuracy of the transformer algorithm in prediction of the hourly level wind energy. Second, with the predicted wind energy, a case study is given to validate the day-ahead daily cycling profile. A typical 1 MVA microgrid is utilized as the simulation model to validate performance of the daily cycling optimization algorithm. The case study results show that the ESS daily cycling can effectively reduce the daily energy expense and help to shave the peak power demand in the grid. [ABSTRACT FROM AUTHOR]

This paper investigates a quantum system described by the Schrödinger equation, utilizing the concept of the quantum Lyapunov function. The Lyapunov function is chosen based on the mean value of a virtual mechanical quantity, where different values of P, the mean value of the virtual mechanical quantity in the Lyapunov function, have an impact on the attractive domain of the quantum system. The selected primary optimization algorithms approximating matrix P are the particle swarm optimization (PSO) algorithm and the simulated annealing (SA) algorithm. This study examines the characteristics of the system's attraction domain under these two distinct algorithms and establishes stability conditions for the nonlinear quantum system. We introduce a method to estimate the size of the attractive domain using the Lyapunov function approach, converting the attractive domain issue into an optimization challenge. Numerical simulations are conducted in various two-dimensional test systems and spin 1/2 particle systems. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ , into the HT-PSO, enhancing the curve's adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. Comparative simulations conducted on 33- and 94-bus systems show an improvement in convergence, demonstrating a more exhaustive exploration of the search space than existing methods documented in the literature based on PSO and variations where functions are proposed for the rate of change of values. [ABSTRACT FROM AUTHOR]

The Marine Predators Algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean's predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators – i.e. Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as solar are becoming increasingly popular worldwide. Mathematical derivation is used to show the structural framework of PV cells and their models with a single-diode configuration. This paper proposed a two-step optimization technique for extracting the unknown parameters of solar PV cells and modules. The implementation of the proposed techniques is to find the unknown parameters of the PV module from Kyocera (KC200GT). First, we configured the single-diode configuration of the PV equation in terms of five unknown parameters ( I ph , I 0 , V t , R s , and R sh ) and in terms of two unknown parameters ( R s and V t ). After that, we implemented the proposed two-step optimization techniques for extracting the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module Kyocera (KC200GT). Also, we used the genetic algorithm (GA) and particle swarm optimization (PSO) techniques to find the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module from Kyocera (KC200GT). The performance of the proposed two-step optimization techniques was compared with the traditional single-stage optimization techniques: particle swarm optimization (PSO), genetic algorithms (GAs), grey wolf optimization (GWO), Villalva's method, Accarino's method, Iterative method, and Silva's method. The test results and the output P–V waveform indicate that the proposed method is more efficient and has a greater impact than the standard techniques. [ABSTRACT FROM AUTHOR]

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU. [ABSTRACT FROM AUTHOR]

The prospect of the energy transition is exciting and sure to benefit multiple aspects of daily life. However, various challenges, such as planning, business models, and energy access are still being tackled. Energy Communities have been gaining traction in the energy transition, as they promote increased integration of Renewable Energy Sources (RESs) and more active participation from the consumers. However, optimization becomes crucial to support decision making and the quality of service for the effective functioning of Energy Communities. Optimization in the context of Energy Communities has been explored in the literature, with increasing attention to metaheuristic approaches. This paper contributes to the ongoing body of work by presenting the results of a benchmark between three classical metaheuristic methods—Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)—and three more recent approaches—the Mountain Gazelle Optimizer (MGO), the Dandelion Optimizer (DO), and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF). Our results show that newer methods, especially the Dandelion Optimizer (DO) and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF), tend to be more competitive in terms of minimizing the objective function. In particular, the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF) demonstrated the capacity to obtain extremely competitive results, being on average 3% better than the second-best method while boasting between around 2× and 10× the speed of other methods. These insights become highly valuable in time-sensitive areas, where obtaining results in a shorter amount of time is crucial for maintaining system operational capabilities. [ABSTRACT FROM AUTHOR]

With respect to a traditional point-to-point (P-P) network, a hub-and-spoke (H-S) network not only uses a smaller number of links/paths but also utilizes the scale economy advantage on consolidated flows on hub–hub links and at hubs. However, the inevitable delays through hubs have always been a critical concern. Therefore, this paper develops an H-S model considering flow delay costs and applies the model to a logistics case in Eastern China. The integer quadratic term in the model's objective function is linearized using the algebraic method. Our model is applied to develop an H-S network for its 13-node express package delivery operation, using the particle swarm optimization (PSO) algorithm. The results show using the H-S can save more than 14.1% of the total cost annually. The model also provides an applied case to the H-S configuration, especially for urban express delivery logistics in China. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices and their integration into critical infrastructure and business operations has rendered them susceptible to malware and cyber-attacks. Such malware presents a threat to the availability and reliability of IoT devices, and a failure to address it can have far-reaching impacts. Due to the limited resources of IoT devices, traditional rule-based detection systems are often ineffective against sophisticated attackers. This paper addressed these issues by designing a new framework that uses a machine learning (ML) algorithm for the detection of malware. Additionally, it also employed sequential detection architecture and evaluated eight malware datasets. The design framework is lightweight and effective in data processing and feature selection algorithms. Moreover, this work proposed a classification model that utilizes one support vector machine (SVM) algorithm and is individually tuned with three different optimization algorithms. The employed optimization algorithms are Nuclear Reactor Optimization (NRO), Artificial Rabbits Optimization (ARO), and Particle Swarm Optimization (PSO). These algorithms are used to explore a diverse search space and ensure robustness in optimizing the SVM for malware detection. After extensive simulations, our proposed framework achieved the desired accuracy among eleven existing ML algorithms and three proposed ensemblers (i.e., NRO_SVM, ARO_SVM, and PSO_SVM). Among all algorithms, NRO_SVM outperforms the others with an accuracy rate of 97.8%, an F1 score of 97%, and a recall of 99%, and has fewer false positives and false negatives. In addition, our model successfully identified and prevented malware-induced attacks with a high probability of recognizing new evolving threats. [ABSTRACT FROM AUTHOR]

Quantum-behaved Particle Swarm Optimization (QPSO) is a meta-heuristic optimization algorithm, which is widely used in many research fields and practical problems due to its flexibility and low computational cost. However, the existing QPSO algorithms and their variants still have problems such as insufficient search capabilities, lack of adaptivity, and prone to stagnation. This paper proposes a novel ensemble algorithm, ACQPSOW-HGS, based on Quantum-behaved Particle Swarm Optimization (QPSO) and Hunger Games Search (HGS). By combining three improvements and introducing three hybrid strategies, our algorithm has made a comprehensive development, effectively improving the stability and solution accuracy in a large number of test functions and the parameter identification application, which is superior compared with many existing algorithms. First, we design the Weibull distribution random number generation operator, the distance-guided adaptive control technique, and the chaotic update mechanism to deal with the weak randomness, insufficient adaptability, and susceptibility to stagnation of the original QPSO algorithm, respectively. Integrating the above improvements, ACQPSOW is proposed as an improved variant of QPSO. Second, the proposed ensemble algorithm ACQPSOW-HGS is built on ACQPSOW and HGS and combined with specific hybrid strategies to add population diversity and improve search efficiency, including the Selection-Crossover-Mutation mechanism, the elite local search mechanism, and the information exchange mechanism. Finally, the experiments, on 23 benchmark functions and the IEEE CEC 2017 test suite, demonstrate that ACQPSOW-HGS outperforms comparison algorithms in terms of convergence speed and solution accuracy through non-parametric statistical tests. Moreover, ACQPSOW-HGS was applied to the fractional-order hyper-chaotic financial system for parameter identification to illustrate the applicability and robustness in solving real-world problems. [ABSTRACT FROM AUTHOR]

With the transformation of transmission and distribution grids into smart grids that are more dominated by renewable energy, power electronics-based inverters that can improve power quality are becoming more visible. In order to maximize the output voltage quality and reduce the total harmonic distortion (THD), efficient operation of inverters is required. Therefore, in this paper, the problem of harmonic elimination in multilevel inverters is solved by using an adaptive grey wolf optimizer with local search. We have performed a grid search-based landscape analysis of the seven-level inverter to understand the behaviour of the proposed algorithm. For verification, the numerical results of the proposed adaptive grey wolf optimizer are compared with those of the original grey wolf optimization algorithm, a modified version of the grey wolf optimization algorithm, the particle swarm optimization algorithm, multi-verse optimization algorithm, and salp swarm algorithm. In the simulations, we solved the optimization model for three different structures of multilevel inverters (7, 11, and 15 levels) by changing the modulation indexes. It is found that the adaptive grey wolf optimization provides lower total harmonic distortion for different modulation indexes. [ABSTRACT FROM AUTHOR]

Deepfakes are digital audio, video, or images manipulated using machine learning algorithms. These manipulated media files can convincingly depict individuals doing or saying things they never actually did. Deepfakes pose significant risks to our lives, including national security, financial markets, and personal privacy. The ability to create convincing deep fakes can also harm individuals' reputations and can be used to spread disinformation and fake news. As such, there is a growing need for reliable and accurate methods to detect deep fakes and prevent their harmful effects. In this paper, a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) with a particle swarm optimization (PSO) algorithm is utilized to demonstrate a deep learning strategy for detecting deepfake videos. High accuracy, sensitivity, specificity, and F1 score were attained by the proposed approach when tested on two publicly available datasets: Celeb-DF and the Deepfake Detection Challenge Dataset (DFDC). Specifically, the proposed method achieved an average accuracy of 97.26% on Celeb-DF and an average accuracy of 94.2% on DFDC. The results were compared to other state-of-the-art methods and showed that the proposed method outperformed many. The proposed method can effectively detect deepfake videos, which is essential for identifying and preventing the spread of manipulated content online. [ABSTRACT FROM AUTHOR]

High-dimensional datasets present significant challenges for classification tasks. Dimensionality reduction, a crucial aspect of data preprocessing, has gained substantial attention due to its ability to improve classification performance. However, identifying the optimal features within high-dimensional datasets remains a computationally demanding task, necessitating the use of efficient algorithms. This paper introduces the Arithmetic Optimization Algorithm (AOA), a novel approach for finding the optimal feature subset. AOA is specifically modified to address feature selection problems based on a transfer function. Additionally, two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision, slow convergence, and susceptibility to local optima. The first enhancement proposes a new method for selecting solutions to be improved during the search process. This method effectively improves the original algorithm's accuracy and convergence speed. The second enhancement introduces a local search with neighborhood strategies (AOA_NBH) during the AOA exploitation phase. AOA_NBH explores the vast search space, aiding the algorithm in escaping local optima. Our results demonstrate that incorporating neighborhood methods enhances the output and achieves significant improvement over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution—especially the emission of greenhouse gases—and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks. [ABSTRACT FROM AUTHOR]

This paper proposes a robust optimization of eight chaotic maps: Logistics, Sine, Gauss, Circle, Tent, Chebyshev, Singer, and Piecewise Maps, for superior image encryption. The proposed model consists of two main processes: chaotic confusion and pixel diffusion. In the chaotic confusion process, the positions of the image's pixels are permuted with the chaotic maps, where the initial condition and the control parameters represent the confusion key. Firstly, the confusion process was performed using the eight chaotic maps without optimization. Then nine metaheuristic optimizers, which are the genetic algorithm, particle swarm optimizations, whale optimization algorithm, dragonfly algorithm, grey wolf optimizer, moth-flame optimizer, sine cosine algorithm, multi-verse optimizer, and ant-lion optimizer, have been used to fine-tune the control parameters of the eight chaotic maps. Then the image's pixel values are changed using the diffusion function in the pixel diffusion process. Multiple performance metrics, such as entropy, histogram, cross-correlation, computation time analysis, the number of pixels change rate (NPCR), unified average changing intensity (UACI), noise attack, data loss, and key analysis metrics, are utilized to evaluate the proposed model. The results demonstrate that the encryption algorithms based on the eight optimized chaotic maps are more resistant to differential attacks than those without optimization. Furthermore, the optimized Gauss chaotic map is the most computationally efficient, while the chaotic circle map has the most robust key. The careful adjustment of initial conditions and control parameters empowers the chaotic maps to create encryption keys with greater randomness and complexity, thereby increasing the security level of the encryption scheme. Experimental analysis indicates that the correlation coefficient values of images encrypted with the proposed scheme are nearly zero, the histogram of the encrypted images is uniform, the execution time of 0.1556 s, the key space of 10^80, NPCR of 99.63%, UACI of 32.92%, and entropy of 7.997. Moreover, the analysis of noise and cropping attacks, along with the comparison with other algorithms, demonstrate the efficiency and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This research paper develops a novel hybrid approach, called hybrid particle swarm optimization–teaching–learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of "exploitation capabilities of PSO" is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, "exploration abilities of TLBO" means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO‐SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO‐SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state‐of‐the‐art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real‐world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta‐heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO‐SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non‐gradient‐based optimizers. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to optimize the thinning of an elliptical cylindrical array (ECA) composed of uniformly stimulated, isotropic antennas with the objective of achieving a directed beam characterized by a significantly reduced relative Sidelobe Level (SLL). The optimization process employs the Novel Particle Swarm Optimization (NPSO) method, which offers a fresh perspective on addressing electromagnetic optimization challenges by its ability to effectively explore solution spaces. By employing the NPSO algorithm, which emulates the collective behavior of swarming particles to search for optimal solutions, this study addresses complex optimization challenges inherent in antenna array design. This study focuses on identifying the optimal combination of ON–OFF components (use the minimum number of ON antenna elements) within the antenna array to produce a radiation pattern exhibiting the greatest decrease in SLL. Additionally, the First Null Beam Width (FNBW) is targeted for optimization without predefined values. The optimization approach also considers the effect of thinned array element spacing on the overall performance metrics. Out of total 36 elements, only 15 elements are switched ON and the remaining of the elements are OFF, so the total reduction or thinning of ECAA is 41.66%. Simulation results demonstrate that the proposed methodology enables a simultaneous reduction of more than half of the antenna array elements while achieving superior SLL minimization. This significant reduction in antenna elements not only contributes to simplifying the array design, but also enhances the array’s beamforming capabilities. [ABSTRACT FROM AUTHOR]

The welding community faces a challenging problem in choosing the best welding methods since they are multi-input processes. Modern manufacturing industries have requirements to get fast and optimum process parameters to utilize complete resources in an optimum way. This work attempts to improve the performance of submerged arc welding (SAW), friction welding (FW), and gas tungsten arc welding (GTAW) processes by optimizing their parameters. The newly developed Rao algorithms and their modified versions known as quasi-oppositional Rao (QO Rao), self-adaptive multi-population elite Rao (SAMPE Rao), and improved Rao (I-Rao) are used. This paper contains four multi-objective optimization case studies of SAW, FW, and GTAW processes. A weighted approach is employed to tackle the multi-objective optimization problems effectively. The outcomes achieved using the Rao, QO Rao, SAMPE Rao, and I-Rao algorithms are compared with those obtained by the established optimization algorithms such as accelerated cuckoo optimization algorithm (ACCOA), cuckoo optimization algorithm (COA), plant propagation algorithm (PPA), teaching–learning-based optimization (TLBO) algorithm, Jaya algorithm, quasi-oppositional Jaya (QO Jaya) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and heat transfer search (HTS) algorithm. The effectiveness of the Rao algorithms and their modified versions has been clearly demonstrated as these algorithms have provided superior solutions while requiring fewer generations to achieve them. [ABSTRACT FROM AUTHOR]

Optimization methods are widely used in various fields to find the best possible solution to a given problem through the minimization or maximization of an objective function while adhering to specific constraints. In this paper, we present a new optimization algorithm, called WOADD, which was designed to handle the challenges of constrained optimization problems that involve dependent data. Unlike traditional algorithms that struggle with data dependencies and valid range constraints, WOADD uses a unique normalization process and a dynamic updating mechanism that accurately considers the interdependencies among features. By calculating a scaling parameter to navigate within feasible regions, WOADD adjusts its search strategy to ensure the preservation of data dependencies and adherence to constraints, ultimately leading to more efficient and precise optimization outcomes. Our extensive experimental analysis, which compared WOADD against other swarm optimization methods using a suite of benchmark functions, demonstrates its superior performance in terms of faster convergence rates, improved solution quality, and enhanced determinism in outcomes. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) paradigm has emerged to connect the number of devices using the Internet resulting in the deployment of smart cities. Cloud computing has been applied to execute the computational demands of IoT devices by collecting data from the physical environment of a smart city. However, cloud computing could not become a proper choice for latency-sensitive applications because of remote cloud data centers. To overcome this challenge, fog computing has emerged to deal with the inherent limitations of cloud computing environment through provision of computing to the edge of a network. However, resource allocation of IoT service requests among fog nodes is considered as an NP-hard problem, which should be addressed in the fog computing environment. In this paper, an efficient optimization approach based on improved binary particle swarm optimization (IBPSO) algorithm has been provided for resource allocation of IoT requests in the hybrid fog–cloud computing environment. The proposed method aims to reduce the service request latency with ensuring load balancing among fog nodes. The performance of the proposed algorithm has been compared by the binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO)-based, and ranked-based resource allocation methods in terms of latency, missed deadline requests, run time, and load balancing. The results show that the proposed algorithm outperformed with an average of around 11 % , 22 % , 21 % , and 22 % percent in the IBPSO-based method rather than the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods, respectively. Moreover, the resource allocation based on IBPSO achieved around 11 % , 28 % , 27 % , and 25 % decline in total latency compared to the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods. Furthermore, the run time of the proposed algorithm could enhance by 45 % , 9 % , and 8 % compared to the BGA-based, BPSO-based, and BGWO-based resource allocation methods. [ABSTRACT FROM AUTHOR]

A novel re-entry trajectory design approach for a winged reusable launch vehicle that satisfies path and terminal state constraints in the height velocity plane is proposed in this paper. A two-phase re-entry trajectory is proposed. A height-velocity plane entry corridor is designed using all path and quasi-equilibrium glide constraints. The proposed method creates the reference trajectory inside the re-entry corridor using three piecewise polynomials, such as linear and logarithmic. A two-parameter search optimization problem minimizes terminal range-to-go error for this reference trajectory generation problem. Improved Search Space Reduction solves this optimization problem. The Improved Search Space Reduction algorithm is compared to the Grey Wolf Optimizer and Particle Swarm Optimisation algorithms to prove its efficacy and superiority. The reference bank angle is modified using feedback linearization and bank reversal logic to create an entry trajectory that meets all path and terminal constraints. The effectiveness of the proposed method has been validated through Monte Carlo simulations that incorporate uncertainties such as variations in initial state, uncertainties in aerodynamics, and atmospheric perturbations. The controller OPAL-RT OP4510 provides bank angle command at every instant based on the current state of the vehicle parameters associated with real-time simulator OP4510. The real-time hardware-in-loop simulation results show that the obtained re-entry trajectory satisfies all path and terminal constraints. [ABSTRACT FROM AUTHOR]

The use of cable-driven parallel robots (CDPRs) has been steadily increasing across various sectors due to their expansive workspaces, impressive payload-to-mass ratios, and cost-effective designs. Controlling these robots, particularly those with substantial actuation redundancy, can present challenges. This research paper proposes the implementation of a fractionalorder proportional-integral-derivative (FOPID) controller to effectively regulate the end-effector of a planar CDPR with four actuation cables. The parameters of the controller are fine-tuned using the particle swarm optimization (PSO) algorithm to ensure optimal performance. The proposed controller's performance is evaluated through two numerical experiments: target tracking and trajectory tracking using a point-to-point approach. Furthermore, a comparative study is conducted to highlight the controller's performance, comparing the proposed FOPID controller with both the classical PID controller and an optimized PID controller. The achieved results demonstrate that the proposed controller exhibits superior performance in terms of tracking accuracy and smoothness of control signals when compared to the other controllers under investigation. As a result, the proposed controller design represents a substantial advancement in control performance and can be regarded as a promising control strategy for CDPRs. [ABSTRACT FROM AUTHOR]

This paper introduces a novel particle swarm optimization algorithm called Mean Prediction Particle Swarm Optimization (MPSO). In MPSO, a potential position is predicted firstly by using the mean value of the population, and then the new movement equation is derived from the difference between the current position and the potential position. In addition, to improve the accuracy of MPSO, a chaos-based local search strategy is adopted. To evaluate the effectiveness of MPSO, we conducted experiments on various benchmark functions and compared its performance with some other existing PSO algorithms. The experimental results demonstrate that MPSO not only achieves better convergence performance but also exhibits higher accuracy. [ABSTRACT FROM AUTHOR]

This paper presents two hybrid optimization methods known as PSOHHO and DPSOHHO optimization algorithms. In the first method, using a number of formulae, the top populations are exchanged between the two algorithms and a new population is created and in the second method, we adopted the parallel optimization and optimized its performance. In this method, unlike other parallel methods, the population does not remain constant. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In these methods, no changes are made to the algorithms. The main goal is to use existing algorithms. These methods attain the optimal solution in the shortest time possible. Two algorithms of particle swarm optimization (PSO) and Harris Hawks's optimization (HHO) are used to present this method and two truss samples and CEC209 are considered to confirm the performance of this method. Based on the results, these methods have rapid convergence speed and acceptable results compared to other methods. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative numerical method for the design and optimization of high-aspect-ratio composite wings equipped with passive control systems, specifically, Folding WingTip (FWT) and Twist WingTip (TWT) devices. The aim is to enhance Gust Load Alleviation (GLA) performance in the baseline wing. Recent numerical studies have indicated that the inclusion of spring devices and wingtip modifications can offer additional benefits in alleviating gust loads during flight. The baseline wing is designed using a comprehensive multi-disciplinary optimization framework, taking into account aerostructural constraints and exploiting the anisotropic properties of composite materials. The proposed methodology integrates Finite Element (FE) software, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses, and Particle Swarm Optimization (PSO). This method, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, facilitated the streamlined design of composite wings with optimized aeroelastic and structural performance. The paper is divided into two main parts. Part 1 introduces a Multidisciplinary Design Optimization (MDO) approach for high-aspect-ratio composite wings, leading to the development of a baseline wing model. Part 2 evaluates the effectiveness of the FWT and TWT devices in alleviating gust loads on the baseline wing, with a focus on the Root Bending Moment (RBM) as a critical criterion for comparison. In wingtip modeling, geometrical nonlinearity is incorporated, and elastic trim is adjusted in each iteration to accommodate shape changes under load and aerodynamic panel movement is synchronized with structural adjustments. [ABSTRACT FROM AUTHOR]

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton’s equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.

This paper presents an improved optimization algorithm for the energy management of a renewable energy solar/wind microgrid with multiple diesel generators applied to off-grid remote communities. The main objective aims to solve the economic emission dispatch problem with a price penalty factor to minimize the energy cost and the emission level. An enhanced metaheuristic optimization algorithm, Lévy arithmetic algorithm, is applied to improve the searchability for optimal solution compared to the conventional arithmetic algorithm. The Lévy arithmetic method is used for the management of the microgrid and compared to other metaheuristic optimization algorithms for the same application. Comparative analysis demonstrates good cost savings using the Lévy arithmetic algorithm, compared to other optimization algorithms such as the arithmetic algorithm, crow search algorithm, hybrid modified grey wolf algorithm, interior search algorithm, cuckoo search algorithm, particle swarm algorithm, colony algorithm, and genetic algorithm. • Proposed an energy management algorithm for the PV/Wind/Diesel Microgrid in off-grid remote communities. • Introduced an enhanced arithmetic optimization approach for addressing economic and emission dispatch. • Evaluated the optimization method's effectiveness and comparative study between the LAA and AOA. • Compared the optimized results of proposed method with other well-known optimization techniques. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive experimental and statistical approach for the analysis of vibration amplitudes in a high-speed rotating shaft employing a squeeze film damper (SFD). The research combines a comprehensive analysis that connects input parameters and response parameters, with a special emphasis on vibration amplitudes along the X and Z axes. This research utilizes the rigorous procedures of Response Surface Methodology (RSM) together with a Box-Behnken design and harnesses the capabilities of Artificial Neural Network (ANN) optimization techniques. The variables under scrutiny encompass critical factors such as shaft rotational speed, extending up to 8000 rpm, oil pressure, with a range extending up to 100 bar, and various oil mix ratios, spanning from 10 % to 50 %. Various statistical measures are computed to assess the errors and coefficients of determination of the projected models. The artificial neural network (ANN) model has shown somewhat reduced prediction errors and a greater coefficient of determination compared to the Response Surface Methodology (RSM) for both the x and z axes of vibration amplitude. The values of mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R-squared) are found for both x and z axes from RSM (3.50, 3.77), (4.50, 4.72), and (0.81, 0.79), (1.88, 1.70), (2.41, 2.12), and (0.94, 0.95), respectively, using the ANN model. The overall mean absolute percentage error (MAPE) from the ANN model of both the x and z axes (9.73 %, 9.38 %) is found to be lower compared to the RSM model (18.18 %–20.50 %). The conclusive research reveals that the artificial neural network (ANN) prediction model outperforms the regression model based on Response Surface Methodology (RSM), exhibiting superior accuracy in predicting vibration amplitudes. The ANN approach is an excellent option for calculating vibration amplitudes in high-speed rotating shafts. Additionally, it provides significant benefits in terms of effectiveness and time economy. This research provides valuable new insights into the most efficient modeling approaches for vibration management and highlights the benefits of using Artificial Neural Networks (ANN) for predictive assessments in this context. Particle Swarm Optimization is used to minimize the vibration amplitude of the shaft along the x and z axes using experimental data. [ABSTRACT FROM AUTHOR]

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

Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and $7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions. [ABSTRACT FROM AUTHOR]

Nature inspired meta heuristics like swarm intelligence (SI), Artificial neural networks (ANN), evolutionary computing (EC) etc. have been used by researchers to solve single and multi-objective optimization problems of different fields. This work uses a novel a-SOS (Adaptive symbiotic organisms search) algorithm for cost optimization of shell and tube heat exchanger. This algorithm is implemented for cost optimization of two benchmark STHX problem which are used by several researchers. Validation of the results is presented by comparing the geometric, flow and operational parameters of the same design problems when solved using particle swarm optimization (PSO), Alpha tuned elephant herding optimization technique (a-EHO) and Gravitation search algorithm (GSA). Result indicates a 4.73% and 11.3% reduction in cost for both the case study respectively when compared to same problems solved using PSO. Although when comparing with a-EHO, results does not indicate any substantial difference. Furthermore, operational, and geometric dimensions are also calculated. This algorithm can be eventually applied to real world design engineering problems. [ABSTRACT FROM AUTHOR]

The quadcopter persist as important roles across diverse applications, and the enhancement of their control efficacy has been the subject of extensive research. In this work, the authors proposed optimal Proportional Integral (PI) controller based Fuzzy Logic Control (FLC) for the roll, pitch, altitude, and yaw motions of the quadcopter system. The proposed technique uses the Particle Swarm Optimization (PSO) algorithm to tune the parameters of the FLC and enhance the quadcopter performance. The simulation results show that the proposed technique achieves smoothness of control and significant improvement over classical techniques, as the rise time and the settling time are reduced by 61 % and 66 %, respectively. These times are important for stabilizing the system's response speed and avoiding overshooting or oscillating. This indicates that the FLCPI-PSO can achieve the desired roll and altitude angles more rapidly and effectively. [ABSTRACT FROM AUTHOR]

This article focuses principally on the comparison baseline and the optimized flow efficiency of the final stage of an axial turbine operating on a gas–steam mixture by applying a hybrid Nelder–Mead and the particle swarm optimization method. Optimization algorithms are combined with CFD calculations to determine the flowpaths and thermodynamic parameters. The working fluid in this study is a mixture of steam and gas produced in a wet combustion chamber, therefore the new turbine type is currently undergoing theoretical research. The purpose of this work is to redesign and examine the last stage of the gas–steam turbine's flow characteristics. Among the optimized variables, there are parameters characterizing the shape of the endwall contours within the rotor domain. The values of the maximized objective function, which is the isentropic efficiency of the turbine stage, are found from the 3D RANS computation of the flowpath geometry changing during the improvement scheme. The optimization process allows the stage efficiency to be increased by almost 4 percentage points. To achieve high-quality results, a mesh of over 20 million elements is used, where the percentage error in efficiency between the previous and current mesh sizes drops below 0.05%. [ABSTRACT FROM AUTHOR]

The coefficients of friction (COFs) is one of the most important parameters used to evaluate the braking performance of a friction brake. Many indicators that affect the safety and comfort of automobiles are associated with brake COFs. The manufacturers of friction brakes and their components are required to spend huge amounts of time and money to carry out experimental tests to ensure the COFs of a newly developed braking system meet the required standards. In order to save time and costs for the development of new friction brake applications, the GRU (Gate Recurrent Unit) algorithm optimized by the improved PSO (particle swarm optimization) global optimization method is employed in this work to predict brake COFs based on existing experimental data obtained from friction braking dynamometer tests. Compared with the LSTM (Long Short-Term Memory) method, the GRU algorithm optimized by PSO avoids the accuracy reduction problem caused by gradient descent in the training process and hence reduces the prediction error and computational cost. The combined PSO–GRU algorithm increases the coefficient of determination (R2) of the prediction by 4.7%, reduces the MAE (mean absolute error) by 14.3%, and increases the prediction speed by 40.1% compared with the standalone GRU method. The prediction method based on machine learning proposed in this study can not only be applied to the prediction of automobile braking COFs but also for other frictional system problems, such as the prediction of braking noise and the friction of various bearing transmission components. [ABSTRACT FROM AUTHOR]

To address the challenges associated with the abundance of features in software datasets, this study proposes a novel hybrid feature selection method that combines quantum particle swarm optimization (QPSO) and principal component analysis (PCA). The objective is to identify a subset of relevant features that can effectively contribute to the accuracy of a predictive model based on an artificial neural network (ANN). The quantum particle swarm optimization algorithm is employed to optimize the selection of features by simulating the behavior of quantum particles in a search space. This approach enhances the exploration and exploitation capabilities, allowing for a more effective identification of relevant features. Furthermore, principal component analysis is integrated into the hybrid method to reduce dimensionality and remove multicollinearity among features, thereby improving the efficiency of the feature selection process. The proposed hybrid method is applied to software defect datasets, where the selected subset of features is fed into an artificial neural network for defect prediction. The performance of the hybrid model is compared with traditional feature selection methods, standalone QPSO, and PCA. Experimental results demonstrate the effectiveness of the hybrid approach in achieving superior predictive accuracy while reducing the dimensionality of the dataset. The proposed approach not only enhances prediction accuracy but also provides a more interpretable and efficient subset of features for building robust defect prediction models. [ABSTRACT FROM AUTHOR]

This work investigates performance improvement the of ring voltage-controlled oscillator. Particle swarm optimization (PSO) techniques are used on both the physical and schematic levels to optimize the design parameter values (width of the NMOS " W n " and PMOS " W p "). By utilizing this strategy, multiple time-taking iterations can be reduced and done in a single cycle, thereby increasing the likelihood that the ring VCO will function to the best of its ability. After getting the optimized values for the design parameters of the ring VCO's, the circuit has been designed for the performance analysis. The same is demonstrated by evaluating the performance of the circuit and confirming the results of the transient analysis and noise analysis carried out with Cadence tools under a variety of different operating conditions using the 45 nm technology process. The analysis has been carried out in a variety of PVT corners (fast–fast, slow–slow, fast–slow, and slow–fast). [ABSTRACT FROM AUTHOR]

This study presents a design optimization of a circumferential feedback channel on the shroud casing between rotor trailing and leading edges to maximize the aerodynamic performance of a single-stage transonic axial compressor, NASA Stage 37, using three-dimensional Reynolds-averaged Navier–Stokes analysis (RANS) with the k-ε turbulence model. The location of the injection and bleeding ports, angles of injection, and bleeding were the most influential factors on the aerodynamic performance and were selected as the design variables. A hybrid genetic algorithm and particle swarm optimization algorithm coupled with three different surrogate models were utilized to maximize the peak adiabatic efficiency and stall margin of the transonic axial compressor. The optimization results for the circumferential feedback channel indicated that an increase in stall margin was achievable with a slightly reduced peak efficiency in comparison with that of the smooth casing. [ABSTRACT FROM AUTHOR]

The two different motion behaviors of seahorses in nature served as inspiration for the seahorse optimization (SHO) method, which is a new metaheuristic swarm intelligence-based approach to solving fundamental engineering problems. In this study, the proportional-integral-derivative (PID) parameters for the simplified speed control of the manipulator joint using squirrel-cage induction motors were calculated with the SHO algorithm. As a result of these calculations, Kp, Ki, and Kd values were obtained as 0.0430, 0.00474, and 0.03254, respectively. Then, the time for the squirrel-cage motor to reach 50 rpm (revolutions per minute) and 90 rpm was calculated with the help of SHO. In PID+SHO operation, the squirrel-cage electric motor reached 50 rpm in 3 seconds and 90 rpm in 8 seconds. In this study, in which the SHO optimization method was used, it was calculated that the acceleration of the squirrel-cage motor and reaching the desired value gave 50% better results compared to the particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid evolutionary algorithm, cultural-particle swarm optimization (C-PSO), which is inspired by the cultural algorithm and the particle swarm optimization algorithm. It is aimed to balance the performance of exploration and exploitation and avoid trapping in the local optima. It introduces a probabilistic approach to update the inertia weight based on the improved metropolis rule. Generating the optimal path without collisions is challenging to ensure vehicles operate safely in real-time implementation. The contributions of C-PSO are to solve the path planning problem of multiple vehicles in modern industrial warehouses, achieving task allocation, fault tolerance and collision avoidance by a dual-layer framework. It was compared with the other algorithms, including PSO, PSO-GA, CA, HS, ABC, HPSGWO, TS and MA, by CEC benchmark functions and statistical tests to demonstrate its great performance with fewer iterations and runtime and the best solutions. It is validated through computational experiments, which involve 15 AGVs and 20 tasks for demonstration. • A novel hybrid bio-inspired technique is proposed based on improving PSO. • Implementation of probabilistic to balance the local and global search abilities. • Task allocation, fault tolerance, and path planning for the multi-AGV system. [ABSTRACT FROM AUTHOR]

[Display omitted] • The most profitable system is on-grid PV-Wind Turbine system with an NPC of $52.8 M. • For 0% and 25% sale constrained systems low penetration systems are optimal. • For 50% sale constrained and unconstrained systems high penetration systems are preferred. • Fire hawk Optimizer, Grey Wolf Optimizer and Particle Swarm Optimizer are used to size the system by power flow analysis. Fire Hawk Optimizer is found to be usable in optimal power flow analysis and system sizing. • Increase in penetrations leads to enlargement of the system in the region with high load demand because of optimizations. Today, with a human population of 7.5 billion, increasing power consumption, and widespread use of high-technology equipment, the need for energy in the world is increasing day by day. The damage to nature caused by non-renewable energy resources and the economic and political disadvantages they cause in countries that must import these resources have led the world to search for alternative energy sources. One of the most important benefits of renewable energy sources is that they can create hybrid energy systems with other energy sources. Hybrid energy systems are structures in that more than one energy generation unit works together to feed the electrical load. In this paper, a hybrid system will be designed by using Homer PRO, to supply the energy consumed in the residential areas of Karaburun and Urla districts of Izmir province in Turkey with hybrid renewable energy systems. Systems of different sizes are designed for 50%, 75%, and 100% penetration rates, and sales to the grid are constrained to 0%, 25%, 50%, and no constraint on sales. Sensitivity analyses have been performed for 10% and 20% more power required by the load. Techno-economic analyses were done for the systems and the optimal system has been identified. The power flow of the optimal system is inspected by defining the network in Python and Fire Hawk Optimizer (FHO), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are used for optimal sizing of the system. The optimal system has a Net Present Cost (NPC) of $52.3 M, 34 wind turbines, and 954 kW PV with conditions of 100% penetration and no restrictions on sales to the grid. FHO proposed 26 wind turbines and 780 kW PVs for Urla and 8 wind turbines, and 174 kW PVs for Karaburun. GWO proposed 25 wind turbines and 747 kW PVs for Urla, 9 wind turbines, and 165 PVs for Karaburun. PSO offered 24 wind turbines and 708 kW PVs for Urla, 10 wind turbines, and 246 kW PVs for Karaburun. When there is no sale to and 25% sales to the grid, it is seen that the optimal scenario is the system with a 50% penetration rate. In fully sale-constrained systems, NPCs are higher than the base system and they can never pay for themselves compared to the base system. It is concluded that smaller-sized systems are more suitable for systems with high constraints. The proposed FHO algorithm and GWO algorithm react to increasing penetration ratios by enlarging the system size in the regions with high load demand. PSO sizes the system like predictions by load demand. FHO reacts quicker to increasing penetrations than PSO and GWO. FHO algorithm is found to be usable to solve optimal power flow problems and optimal system sizing. [ABSTRACT FROM AUTHOR]

Increased demand in renewable power move towards the balancing conventional dependent energy source lead the increase in technical development in reliable power generation. This model contains the wind speed variation, capacity, uncertainty, and production are considered in system evaluations to bring the wind power to more sustainable energy. More reliable VSC HVDC technology in power transmissions for longer distances is preferred to transmit the renewable generated energy to remote end areas. Authors introduced particle swarm optimization method into the wind integrated HVDC System. The system is developed in MATLAB/Simulink to evaluate the wind power generation performance and Transmission performance. The simulation results are correlated and validated with the existing VSC HVDC link parameters. The supremacy in wind energy with particle swarm optimization performance is validated and more viable to integrate wind energy system with VSC HVDC system to transmit the reliable power to remote areas. [ABSTRACT FROM AUTHOR]

Many significant parameters show the performance of batteries used in important fields such as biomedical systems, energy storage units, electric vehicle technologies, and advanced space studies. Two essential indicators among these parameters are open-circuit voltage and state of health. In this study, it is tried to estimate open-circuit voltage and state of health with high accuracy by applying optimization methods on the Thevenin electrical equivalent circuit model of batteries. The parameter values obtained by examining the discharge tests of the Li-ion battery cell with 2A constant current during the 150 charge/discharge cycle time at 25 °C are transferred to the electrical equivalent circuit model. Curve-fitting method, artificial bee colony, particle swarm optimization, dragonfly algorithm, and genetic algorithm have been studied in the prediction operation of open-circuit voltage and state of health which is defined based on state of charge, number of cycles, rated current capacity, and time. Comparisons are made considering the absolute error values, the smallest value of the sum of the squares of the errors, the response speed of the methods, and the correct estimation ability. Ultimately, it is aimed to obtain the most suitable method. [ABSTRACT FROM AUTHOR]

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively. [ABSTRACT FROM AUTHOR]

The gait cycle of 25-degree of freedom (DOF) humanoid robot, namely NAO robot, consists of single support phase (SSP) and double support phase (DSP). Both dynamic and stability analyses are carried out for this robot to determine its power consumption and dynamic stability margin, respectively. Constrained single-objective optimization problems are formulated for the SSP and DSP separately and solved using particle swarm optimization (PSO) and genetic algorithms (GA). A performance index, other than the fitness function, consisting of constraint values and maximum swing height, is also considered to compare PSO and GA-obtained optimal solutions. PSO is able to find the trajectories that offer higher swing height for nearly similar power consumption during SSP. A performance assessment of each algorithm based on the best fitness values in each generation across several runs is also carried out. These values are compared using the Wilcoxon rank-sum test, and PSO is found to be statistically better than GA. The optimal solutions from the simulations are tested using the Webots simulator to validate their efficacy on stability. Moreover, an investigation of the influence of gait parameters on power consumption during SSP and DSP reveals that the humanoid robot with a higher hip height, lower swing height, and slow pace consumes less power. The methodology developed in this is generic and can be easily extended to other robots. [ABSTRACT FROM AUTHOR]

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players' energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder–Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87 ± 61.42 and root mean squared error (RMSE) of 520.69 ± 88.66 achieved by our model, as opposed to the B 1 MAE of 429.04 ± 84.87 and RMSE of 581.34 ± 185.84 , and B 2 MAE of 421.57 ± 95.96 and RMSE of 613.47 ± 300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players' responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics. [ABSTRACT FROM AUTHOR]

In this article, the control of a DC/DC converter was carried out using the proposed methods of conventional PI, PSO-based PI, PSO-based FOPI, GA-based PI, and GA-based FOPI controllers in order to improve the performance of PEMFCs. Simulink models of a PEMFC model with two inputs—hydrogen consumption and oxygen air flow—and with controllers were developed. Then, the outputs of a system based on conventional PI were compared with the proposed methods. IAE, ISTE, and ITAE were employed as fitness functions in optimization algorithms such as PSO and GA. Fitness function value, maximum overshoot, and rising time were utilized as metrics to compare the performance of the methods. PI and FOPI parameters were optimized with the proposed methods and the results were compared with traditional PI in which the optimum parameters were determined by an empirical approach. This research study indicates that the proposed methods perform better than the conventional PI method. However, it becomes apparent that the GA-FOPI approach outperforms the others. The simulation result also shows that the PEMFC model with conventional PI and FOPI controllers in which the controller parameters are tuned using PSO and GA has an acceptable control performance. [ABSTRACT FROM AUTHOR]

The optimization of the production scheme for enhanced geothermal systems (EGS) in geothermal fields is crucial for enhancing heat production efficiency and prolonging the lifespan of thermal reservoirs. In this study, the 4100–4300 m granite diorite stratum in the Zhacang geothermal field was taken as the target stratum to establish a numerical model of water-heat coupling of three vertical wells. However, relying solely on numerical simulation for optimization is time-consuming and challenging for the determination of the globally optimal production plan. The present study proposes a comprehensive evaluation method for optimizing the performance of EGS power generation based on the integration of particle swarm optimization with backpropagation neural network (PSO-BPNN) and analytic hierarchy process (AHP). Five different PSO-BPNN models were constructed based on the numerical simulation data to predict different EGS power generation performance indexes, including the production temperature, the injection pressure, the total electricity generation, the electric energy efficiency and the levelized cost of electricity. Based on these PSO-BPNN models, the weights of various thermal development evaluation indexes were calculated by AHP to conduct a comprehensive evaluation of the power generation performance of the three vertical wells EGS. The results show that the PSO-BPNN model has good prediction accuracy for EGS prediction of various performance indicators, with a coefficient of determination (R2) exceeding 0.999. The AHP evaluation of all production schemes reveals that the optimal power generation scheme entails a well spacing of 580 m, water injection rate of 56 kg/s, injection temperature of 38 °C and fracture permeability of 2.0 × 10−10 m2. Over a span of 30 years, this scheme can provide a total power generation capacity amounting to 1775 GWh, with an associated LCOE value of 0.03837 USD/kWh. This not only provides a reference for the development and optimization of geothermal systems in the Zhacang geothermal field but also provides a new idea for the optimization design of other geothermal projects. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ, into the HT-PSO, enhancing the curve’s adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. Comparative simulations conducted on 33- and 94-bus systems show an improvement in convergence, demonstrating a more exhaustive exploration of the search space than existing methods documented in the literature based on PSO and variations where functions are proposed for the rate of change of values.