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The Singing Muscle Ability Training System (SMATS) represents a cutting-edge technological solution dedicated to honing an individual's vocal prowess. By employing a sophisticated combination of techniques, exercises, and methodologies, this system strategically targets and cultivates the muscle groups integral to singing. In its innovative approach, SMATS integrates Artificial Intelligence (AI) to elevate vocal capabilities further. The proposed SMATS-AI-VONN-GSOA stands out by incorporating the Variational Onsager Neural Network (VONN) alongside the Golden Search Optimization Algorithm (GSOA). This amalgamation tailors training routines based on the user's progress and preferences, emphasizing the development of muscle memory and control for enhanced vocal performance. Noteworthy is the system's capacity to analyze data through AI, enabling the creation of personalized training plans. In comparative evaluations, the SMATS-AI-VONN-GSOA method demonstrates a significant performance boost, surpassing existing methods like SMATS-AI-CNN, SMATS-AI-DNN, and SMATS-AI-DNN-HSV by 26.78%, 29.55%, and 21.41% in accuracy, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a new metahaeuristic algorithm named particle swarm optimization and chaotic gravitational search algorithm (PSO-CGSA) for solving the combined economic and emission dispatch (CEED) problem. First, we determine the efficiency and effectiveness measures of the algorithm and compare it with other well-known algorithms. Then, we analyze the obtained solutions using the statistical procedure proposed in the paper. The proposed procedure contains the following: (i) the behavior analysis of the algorithms when solving the CEED problem, using non-parametric tests, and (ii) the ranking of the algorithms using the PROMETHEE/GAIA multi-criteria decision-making method. The behavior analysis is performed for two cases: (i) when solving individual variants of the CEED problem (single-problem analysis) and (ii) when solving a set of CEED variants (multiple-problem analysis). The results of the applied procedure for the test system with six generators show that PSO-CGSA has (i) the best solution for each tested variant of the CEED problem; (ii) the best standard deviation, mean value, error rate, and behavior for the CEED variant with a bi-objective function that simultaneously minimizes fuel cost and emission, taking into account the valve point effect; and (iii) the best rank when solving a set of CEED variants. [ABSTRACT FROM AUTHOR]

The present study introduces a novel penalty-free hybrid metaheuristic, differential evolution–krill herd algorithm (DE-KHA) a multiobjective algorithm (MOA) for the reliability-based optimal design of water distribution networks (WDNs). The selection mechanism of the DE-KHA MOA is equipped with nondominated sorting and density estimation schemes to obtain a Pareto front with diverse trade-off solutions. The introduced penalty-free scheme allows the algorithm to search for hydraulically functional nondominant solutions. With this implemented framework of the DE-KHA MOA, the WDN design problem is formulated with two conflicting objectives, (1) minimizing pipe investment cost; and (2) maximizing network's reserve through flow entropy (SF), a surrogate reliability measure. The application and computational efficiency of the proposed MOA are demonstrated by employing two well-established benchmark case studies. Parallelly a trial-based approach for conducting sensitivity analysis for MOAs is demonstrated. The computational results manifest the efficacy of the penalty-free DE-KHA MOA in resulting in the Pareto front comprising the hydraulic feasible nondominant solutions of disparate trade-off relationships. As well, the results highlight the applicability of the proposed sensitivity analysis approach in improving the convergence behavior of the MOA. Following the reliability-based design, to assess the flexibility of the solved nondominant solutions under critical scenarios above design standards, the study performed a posterior performance investigation using pressure-driven analysis. The results demonstrate the effectiveness of the proposed approach with supported subjective knowledge in selecting robust design alternatives that are mechanically and hydraulically reliable. Moreover, the proposed approach eases the effort and perplexing state of handling increased nondominant design options with larger-size WDNs. Practical Applications: The present study introduces a computationally efficient multiobjective algorithm for the economical and reliable design of water distribution networks. By considering the reliability measure (flow entropy) in the design framework, the study focuses on proposing design alternatives for water utilities with inherent redundancy and tolerance to failures (mechanical). Following optimal design, the study proposes a performance investigation model that is useful in assessing the network's integrity, detailing the quantitative and qualitative performance of alternate designs in the face of uncertainties. This particular framework is notably more useful in proposing water utilities, a set of economical yet robust (flexible) design alternatives, which quickly adapt to uncertainties. As well, the proposed design approach eases the effort of designers in handling the increased design options with larger-size networks. [ABSTRACT FROM AUTHOR]

In this paper, multilevel thresholding with Tangent Hybrid Leader Coronavirus Herd Optimization (THLCHO) is introduced for the image segmentation of foreground and background, The input image is passed to the pre-processing that is done by the adaptive wiener filtering (AWF) technique and the Region of Interest (RoI) extraction. Multilevel thresholding approaches such as threshold and threshold levels are employed to segment the images by entropy-based Kapur, and ostu thresholding, masi entropy and Renyi's entropy, then the threshold values are optimally selected by THLCHO and considered as the output of the techniques. Secondly, the pre-processed image is subjected to the background and foreground image segmentation utilizing the entropy-based Fuzzy C-Means (EFCM) algorithm, which is assumed as an output of EFCM. Lastly, both outcomes are merged to produce the resultant output. The evaluation metrics employed for Multithresholding- THLCHO, namely, dice co-efficient, peak signal-to-noise-ratio (PSNR) and uniformity measure acquired the maximal values of 0.900, 38.274 dB and 0.918, respectively. [ABSTRACT FROM AUTHOR]

Predicting wind speed is a complex undertaking influenced not only by the wind-speed sequence itself but also by various meteorological factors. This paper introduces a novel multivariate deep-learning neural network prediction model that takes into account not only historical wind-speed data but also a series of meteorological features relevant to wind speed. The meteorological features associated with wind speed are initially extracted using the random forest algorithm (RF). Subsequently, Variational Mode Decomposition and Autocorrelation Function analysis are employed for noise reduction in the wind-speed series. Finally, the wind-speed series are predicted using a Gated Recurrent Unit (GRU) deep-learning neural network, and an Improved Sparrow Search Algorithm is proposed to optimize the four parameters of the GRU. To validate the predictive performance of the model, experimental data from three cities in China, Shenyang, Dalian, and Yingkou, are utilized. The experimental results demonstrate that our proposed model outperforms other models, as evidenced by four key performance indicators. [ABSTRACT FROM AUTHOR]

A multi-strategy improved honey badger algorithm (MIHBA) is proposed to address the problem that the honey badger algorithm may fall into local optimum and premature convergence when dealing with complex optimization problems. By introducing Halton sequences to initialize the population, the diversity of the population is enhanced, and premature convergence is effectively avoided. The dynamic density factor of water waves is added to improve the search efficiency of the algorithm in the solution space. Lens opposition learning based on the principle of lens imaging is also introduced to enhance the ability of the algorithm to get rid of local optimums. MIHBA achieves the best ranking in 23 test functions and 4 engineering design problems. The improvement of this paper improves the convergence speed and accuracy of the algorithm, enhances the adaptability and solving ability of the algorithm to complex functions, and provides new ideas for solving complex engineering design problems. [ABSTRACT FROM AUTHOR]

The model-implied simulation-based power estimation (MSPE) approach is a new general method for power estimation (Irmer et al., 2024). MSPE was developed especially for power estimation of non-linear structural equation models (SEM), but it also can be applied to linear SEM and manifest models using the R package powerNLSEM. After first providing some information about MSPE and the new adaptive algorithm that automatically selects sample sizes for the best prediction of power using simulation, a tutorial on how to conduct the MSPE for quadratic and interaction SEM (QISEM) using the powerNLSEM package is provided. Power estimation is demonstrated for four methods, latent moderated structural equations (LMS), the unconstrained product indicator (UPI), a simple factor score regression (FSR), and a scale regression (SR) approach to QISEM. In two simulation studies, we highlight the performance of the MSPE for all four methods applied to two QISEM with varying complexity and reliability. Further, we justify the settings of the newly developed adaptive search algorithm via performance evaluations using simulation. Overall, the MSPE using the adaptive approach performs well in terms of bias and Type I error rates. [ABSTRACT FROM AUTHOR]

The stability of gravity-retaining structures has traditionally been checked within the limit equilibrium framework. However, limit equilibrium methods require that prior assumptions be made regarding the collapse mechanism of the wall–soil system. Using the strength reduction method (SRM) with finite-element analysis (FEA) to analyze the stability of a wall does not require the collapse mechanism of the wall–soil system or even its general form or location to be known a priori; instead, it results from the analysis automatically. This paper uses the results of SRM-based FEA simulations performed using a sophisticated two-surface-plasticity sand model to evaluate the stability and factor of safety of gravity-retaining walls. The SRM-based FEA simulations were performed via ABAQUS and Python scripting using novel strength reduction techniques for both the backfill soil and the gravity wall. The generalized bisection method was used as the search algorithm to find the minimum backfill relative density DR,min or the minimum wall width Bmin just prior to the instability and collapse of the wall–soil system. The stability of the wall–soil system was detected based on the convergence (or lack thereof) of the Newton–Raphson (N-R) iterative solution implemented in an implicit FE modeling scheme. The proposed approach does not rely on the assumption that a wall would independently achieve overturning, sliding, or bearing capacity limit states and is therefore more accurate in describing wall stability. [ABSTRACT FROM AUTHOR]

Recently researchers proposed truck and drone coordination to increase delivery efficiency and suggested various truck-drone routing problems. In this paper, we also focused on truck and drone coordination and introduced the paired pickup and delivery problem with profit in a two-echelon delivery system. To solve the problem, we propose a hybrid variable neighborhood search algorithm. For this algorithm, we adapted existing neighborhood search operators from the literature and considering the structure of the proposed problem, developed new neighborhood search operators. Also, we have carried out numerous computational experiments to evaluate the proposed solution methods' performance, where the results show the efficiency of the proposed algorithms. The results highlight that in the paired pickup and delivery problem, for small values of drone operational costs, employing the two-echelon truck and drone routing system increases the profit by up to 5.6 percent in comparison to the vehicle routing system with drones. [ABSTRACT FROM AUTHOR]

The protection relay is essential for ensuring stability and detecting faults in power systems. This study presents the Jellyfish Search Optimization algorithm (JSO) as a method for optimizing the configurations of directional overcurrent relays (DOCRs) in electric power systems. The settings of DOCRs are plug setting current (PS) and time multiplier setting (TMS). The main goal is to reduce the duration of primary relay operations time, thereby improving coordination efficiency. The proposed JSO is implemented on IEEE eight-bus and fifteen-bus networks, and its performance is evaluated in comparison with existing methodologies. The results demonstrate a notable decrease in the duration of primary relay operation, ranging from 4.48 to 41.77%, when compared to previous algorithms in the 8-bus scenario. However, in the case of the 15-bus, the improvement in total operation time of primary relay ranged between 0.87 and 33.80%. However, when compared with previous studies, the JSO underperformed. The study employs ETAP software to perform load and fault current calculations, and MATLAB to implement algorithms. This research provides valuable insights into the coordination of optimal DOCR. [ABSTRACT FROM AUTHOR]

The variability and unpredictability of wind power generation present significant challenges for grid management and planning. Enhancing the accuracy of wind power forecasting is crucial for improving the reliability of renewable energy systems. To enhance the accuracy of temporal wind power predictions, the IVMD–DCInformer–HSSA framework has been introduced. Initially, the original wind power data is decomposed into multiple intrinsic mode function (IMF) components using the improved variational mode decomposition (IVMD) technique. Subsequently, the Sparrow search algorithm (HSSA) is employed to optimize the parameters of the enhanced Informer deep neural network, which are then integrated into the improved Informer model. The predictions of each IMF component resulting from the IVMD decomposition are then combined to generate the final prediction outcome. The experimental results show that the R‐squared value of the proposed combined model is increased to 0.9903, and the accuracy is increased by 1%–3% compared with other models, which has a good prediction effect. [ABSTRACT FROM AUTHOR]

This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS‐AW). MACS is a multi‐agent memetic scheme for multi‐objective optimization originally developed to mix local and population‐based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub‐problem the agent had to solve; (ii) the population‐based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS‐AW is compared against some state‐of‐art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS‐AW is applied to the solution of two real‐life optimization problems and compared against MACS2.1. It will be shown that MACS‐AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS‐AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS‐AW and its predecessor obtain same results. [ABSTRACT FROM AUTHOR]

Online voltage transformer error prediction is an important research direction in the field of smart grids. This article mainly focuses on the online error prediction and the parallelization method of voltage transformers. First, an optimized multi-layer perceptron model based on the sparrow search algorithm (SSA) is proposed. The weight initialization process is optimized using the SSA to improve the prediction accuracy of the multi-layer perceptron. Considering the massive amount of data in real-world scenarios, a distributed sparrow search optimization algorithm for the multi-layer perceptron model was then developed, and the acceleration and scalability were tested on different data scales. In addition, transformer error prediction experiments were conducted to demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an ultra-low-voltage 10-bit successive approximation-register analog-to-digital converter (SAR ADC) based on the binary search algorithm for biomedical applications. An energy-efficient DAC switching scheme for a fully differential SAR ADC is proposed, which achieves a 99.8% reduction in DAC switching energy compared to conventional SAR ADC. In this design, by using a dummy capacitor split technique, an attempt has been made to reduce the capacitor of the most significant bit, resulting in a 92.87% reduction in the total number of capacitors compared to conventional design. In the proposed structure, the common-mode voltage of the comparator is approximately constant. The maximum voltage variation in the proposed switching scheme is Vref/2. Additionally, power consumption has been reduced by implementing the power gating technique in the control logic part. The proposed converter with a sampling frequency of 1 kS/s and a supply voltage of 0.2 V has been designed and simulated in TSMC 65nm CMOS technology. Both analytical calculations and simulation results confirm the effectiveness of the proposed switching scheme. Ultimately, the proposed scheme achieves a power consumption of 2.08 nW and a Figure of Merit (FoM) of 5.39 fJ/conversion-step. In comparison with the state-of-the-art, the proposed design has demonstrated excellent performance in achieving optimal power. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-output DC–DC power conversion system based on Photovoltaic (PV) technology. PV panels are connected to a series compensated Buck-Boost Converter (SCBBC) to harvest solar energy, while a sliding mode controller (SMC) ensures maximum power point tracking (MPPT). During the intermediate phase, a synchronized Buck-Boost Converter (SBBC) topology is used to ensure effective charging and discharging of batteries. Additionally, a PI-SMC hybrid control strategy is applied at the back end to the Super Lift Luo Converter (SLLC) to maintain the load voltage at a desired value. A Gravitational Search Algorithm (GSA)-based PI controller controls the input current, while the output voltage is controlled by the outer loop (SMC). We use a single-loop SMC approach to validate the performance of the proposed dual-loop control scheme. According to the presented results, the dual-loop control scheme demonstrated higher dynamic performance in controlling input current and output voltage. [ABSTRACT FROM AUTHOR]

Establishing an energy harvesting device to supply sustainable energy to deep sea sensors is a challenging task due to the complex subsea marine phenomenon. To overcome this, we propose a two-magnet KMC energy harvester hanging on the cabin of the ship with a spherical bearing. The magnets connected with springs move vertically up and down when the ship is periodically actuated by surface waves, generating electricity to power the deep sea sensors through a cable. We present a case study using wave speed (Uow = 2.0 m/s), wave amplitude (Ho = 0.4 m), and wave length (λ = 4.0 m) to demonstrate the efficacy of the two-magnet energy harvester. Before optimizing the energy harvester, a sensitivity analysis is conducted on the electrical power with respect to marine parameters and the energy harvester's geometric parameters. To maximize the electrical power, we adopt a Cuckoo Search Algorithm as an optimizer. By using the objective function of root-mean-square electrical power in conjunction with the Cuckoo Search, we obtain optimal electrical power. Simulations reveal that the energy harvester produces 0.1497 Watts of power. [ABSTRACT FROM AUTHOR]

Herein, several semianalytical formulae for the optimal distribution of elastic moduli in 2D or 3D domains occupied by nonhomogeneous, least compliant bodies are presented and three different methods for recovering microstructures predicted by the isotropic material design method are proposed, which is a stress‐based variant of topology optimization for isotropic bodies. In all five presented variants of the topology optimization of elastic bodies, the numerical implementation of these formulae requires only the use of any algorithm of searching for the minimum of functional in multidimensions without any constraints, even box constraints on design parameters. For each variant considered, the formulae defining this functional are defined analytically. All results concern the design of composite structures that maximize their stiffness (equivalently minimize their compliance) while satisfying a certain isoperimetric condition introducing the upper limit of the available material resource. The article pays special attention to the fact that the stiffest, heterogeneous isotropic elastic material has almost always auxetic properties. Based on the asymptotic homogenization method and adopted bending‐free lattice model, an algorithm for the numerical recovering of nonhomogeneous isotropic microstructure in the entire domain occupied by the plate is proposed, with a fairly easy‐to‐implement ability to model an auxetic behavior. [ABSTRACT FROM AUTHOR]

Parametric timed automata (PTA) extend timed automata (TA) with parameters instead of fixed timing constraints, providing the flexibility to accommodate uncertainties during the design phase. Once a parametric model is obtained, the next step is finding the optimal parameters such that the resulting TA satisfies the specifications. This paper introduces a new algorithm for determining parameters from safety specifications for PTA with bounded integer parameters and no nested cycles. The algorithm searches for unsafe paths through a depth-first search and generates parameter constraints. In particular, the realizability of simple and cyclic paths are encoded via mixed integer linear programming and non-linear programming problems. Then, the parameter constraints rendering the path unrealizable are derived via quantifier elimination. The accumulated constraints through the depth-first search guarantee that a parameter valuation satisfying these constraints solves the synthesis problem. The results are illustrated over benchmarks. [ABSTRACT FROM AUTHOR]

The Colored bin packing problem (CBPP) is a generalization of the Bin packing problem (BPP). The CBPP consists of packing a set of items, each with a weight and a color, in bins of limited capacity, minimizing the number of used bins and satisfying the constraint that two items of the same color cannot be packed side by side in the same bin. In this article, we proposed an adaptation of BPP heuristics and new heuristics for the CBPP. Moreover, we propose a set of fast neighborhood search algorithms for CBPP. These neighborhoods are applied in a meta-heuristic approach based on the Variable neighborhood search (VNS) and a matheuristic approach that combines linear programming with the meta-heuristics VNS and Greedy randomized adaptive search (GRASP). The results indicate that our matheuristic is superior to VNS and that both approaches can find near-optimal solutions for a large number of instances, even for those with many items. [ABSTRACT FROM AUTHOR]

Sports timetabling is a complex and challenging problem. The latest open benchmark dataset for the sport timetabling problem is from the International Timetabling Competition (ITC) 2021. Due to its complexity, only a few approaches have successfully generated feasible solutions for the problems in this dataset, as reported in scientific literature. To the best of our knowledge, there is only one study in the literature that has successfully generated feasible solutions for all 45 problems in the dataset. In this paper, we propose our novel efficient algorithm based on the Iterated Local Search algorithm to solve the ITC 2021 benchmark dataset. Unlike prior successful approaches that combined metaheuristics with an exact approach, our proposed approach is solely metaheuristic. Our contribution includes the design of strategies for both perturbation and local search phases, coupled with the integration of shuffling strategies. The experimental results show that our proposed algorithm is remarkably successful in generating feasible solutions for all 45 problems present in the ITC 2021 dataset. [ABSTRACT FROM AUTHOR]

The main aim of this article is to use a new metaheuristic algorithm for the optimum design of truss structures. The artificial rabbits optimization (ARO) algorithm is used, in which rabbits' natural survival strategies, such as detour foraging and random hiding, are considered in developing the main search loop of the algorithm. An improved version, I-ARO, is also proposed, using the diagonal linear uniform (DLU) initialization process instead of conventional random initialization to enhance the overall performance and convergence behaviour of the ARO algorithm. For numerical investigations, five well-known benchmark truss structures with 10, 37, 52, 72 and 120 bars are determined, considering the natural frequencies of the structures as design constraints. I-ARO is shown to be capable of providing better results than the standard ARO and other approaches in the literature. This demonstrates the capability of the DLU initialization method in enhancing the performance of the I-ARO algorithm. Highlights Optimization of truss structures is investigated Improved Artificial Rabbits Optimization (IARO) algorithm is proposed Diagonal Linear Uniform (DLU) initialization process is implemented in IARO [ABSTRACT FROM AUTHOR]

This article addresses the permutation flowshop scheduling problem with the objective of minimizing the total completion time subject to a makespan constraint. The makespan is related to system utilization while the total completion time is related to the waiting time, and hence to the work in process (WIP); in real contexts, focusing on both total completion time and makespan allows a good trade-off between WIP and utilization to be found. Two local search algorithms are developed in the paper and, by using an extensive computational experience on literature benchmark instances, they are proved to be able to find good solutions both for regular and no-wait flowshops. [ABSTRACT FROM AUTHOR]

Given the non-stationarity, nonlinearity, and high complexity of polysilicon prices in the photovoltaic (PV) industry chain, this paper introduces upstream and downstream material prices of the PV industry chain and macroeconomic indicators as influencing factors. The VMD–SSA–LSTM combination model is constructed to predict polysilicon prices, which is based on Variational Mode Decomposition (VMD) and utilizes the Sparrow Search Algorithm (SSA) to optimize the Long Short-Term Memory (LSTM) network. The research shows that decomposing the original polysilicon time series using the VMD algorithm effectively extracts the main features of polysilicon price data, reducing data instability. By optimizing the learning rate, hidden layer nodes, and regularization coefficients of the LSTM model using the Sparrow Search Algorithm, the model achieves higher convergence accuracy. Compared to the traditional LSTM model and VMD–LSTM model, the VMD–SSA–LSTM model exhibits the smallest error and the highest goodness-of-fit on the polysilicon dataset, demonstrating higher predictive accuracy for polysilicon prices, which provides more accurate reference data for market analysis and pricing decisions of the polysilicon industry. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy and poor stability of the current algorithms in solving multi-dimensional knapsacks, especially the inability to effectively solve super-large-scale arithmetic cases, this paper proposed a new type of hu- man learning optimization algorithm. Firstly, the noval human learning algorithm used a hash function based on the memory theory in cognitive psychology to represent the memory behaviour of human beings in the learning process, avoiding repeated searches and improving the algorithm's search group diversity. Secondly, the algorithm used the contrastive cognition theory from cognitive psychology to adaptively adjust the learning operator selection strategy. Finally, the algorithm used a variable neighborhood search operation to enhance the algorithm s local search capability. This paper conducted numerical experiments using a standardized test dataset of a total of 76 multidimensional knapsack problems that covered small, medium, large, and very large scales. Experiments compared the new algorithm with binary particle swarm algorithms, genetic algorithms, human learning algorithms, and human learning algorithms that incorporated the psychology of learning. The results show that the new algorithm is able to solve the four scale instances efficiently. Compared with other algorithms, the new algorithm has higher ac- curacy in finding the optimum and better stability. In addition, this paper analyzed three proposed optimization strategies to test their effectiveness in improving the algorithm's search performance. [ABSTRACT FROM AUTHOR]

To efficiently track the evolving Pareto frontier in dynamic multiobjective optimization problems over time or environment, this paper proposed a new dynamic multiobjective optimization algorithm, called HDDMOEA, based on environment sensing and prediction correction. This algorithm incorporated three main strategies. Firstly, it employed the Wilcoxon signed rank test to detect environmental changes and introduced a new environment sensing operator to determine the intensity of these changes. Secondly, it constructed the Holt differential prediction correction model to predict the positions of population individuals in the next time window, making corrections based on a reference point during the prediction process to enhance prediction accuracy and accelerate the search speed of the algorithm. Then, it introduced a new mutation method that injected different mutated individuals based on the intensity of environmental changes to maintain population diversity, thus reducing the likelihood of the population falling into local optima. To verify HDDMOEA's effectiveness, researchers conducted comparative analysis between HDDMOEA and five advanced prediction algorithms on the FDA and dMOP test sets. The results demonstrate that HDDMOEA effectively and dynamically balances the diversity and convergence of populations during the search process, continuously tracking the Pareto frontier and outperforming the other five comparative algorithms. [ABSTRACT FROM AUTHOR]

In order to allocate airport flight time resources more scientifically and reasonably, a flight time optimization method considering transit passengers is proposed. First, a flight connection quality assessment model considering the effectiveness of candidate trips is constructed by combining the travel choice characteristics of transit passengers. Second, a dual-objective model is established to maximize the flight connection quality and minimize the maximum flight time offset, and constraints such as the type of transit passengers and the type of connecting flights are taken into account, so that the optimization model is more in line with the actual transit operation situation of hub airports. Finally, an adaptive large neighborhood search algorithm is designed to solve the problem, and the model and algorithm are applied to Beijing Capital International Airport for case study. The results show that the constructed model can provide passengers with more effective trips, increase the number of reachable airports, and improve airport connectivity based on the differences in the choices of transit passengers in terms of time, space, the impact of direct flights, and service quality. The optimized effective passenger journeys and flight connection quality have been improved by 21.74% and 21.57%, respectively, and the connection quality of about 79.66% of the flights has been improved. It can be seen that the optimized flight schedules are more reasonable in terms of temporal and spatial distribution, which can significantly improve the quality of connection between flights and provide more effective transfer options for transit passengers. [ABSTRACT FROM AUTHOR]

Aiming at the noise, cost and safety problems of current unmanned aerial vehicle (UAV) operation within cities, this paper divides the airspace into layers by altitudes and proposes an operation cost and crash risk model based on noise protection zones under aerodynamic and other constraints. This model satisfies the standard operation conditions of UAVs, and reduces the noise impact on the environment and the ground population, as well as operation cost and crash risk. Thus, based on the noise protection zone, the improved Dubins path planning method is used. It combines the Dubins path planning idea with the tangent line of the geometric circle, and adds node processing to enrich the optional paths for UAV operation and optimize the paths. As the Dijkstra algorithm is used as the the best path searching algorithm, and the lowest total cost is set as the goal, a UAV optimal operation path is searched and compared with the A* algorithm. Simulation experiments verify the effectiveness of the proposed model and the improved method. They reduce the noise impact of UAV operation and the operation cost, and improve the safety and efficiency of the operation. The results indicate that the optimal operation altitude of the example UAV, a biplane medium-sized UAV with a mass of 15 kg and a paddle disk area of about 1.313 m², is 40 m, and its operation minimum total cost is 5.42; compared with the results in the other altitude layers, the total operation cost is reduced by 37.56% at most and 5.91% at least. [ABSTRACT FROM AUTHOR]

This paper presents an innovative readout scheme that utilizes a pair of differential‐clocks‐assisted time‐to‐digital converter (DCA‐TDC) in CMOS image sensors (CISs). The DCA‐TDC utilizes only half the number of ordinary TDC delay chain units by employing a binary‐weighted search algorithm to determine the most significant bit (MSB) for fine quantization of a single‐slope analog‐to‐digital converter (SS ADC). Additionally, the layout area and dynamic power introduced by the improved DCA‐TDC delay chain are halved compared to an ordinary TDC delay chain. The proposed SS ADC is designed and simulated using the 0.11 μm standard CMOS proces, achieving an 11‐bit ADC with a column‐level power consumption of 65.4 μW, and a row conversion time of 2.72 μs, within a design environment featuring an analog voltage of 3.3 V, a digital voltage of 1.5 V, a clock frequency of 62.5 MHz, and a temporal resolution of 500 ps. Furthermore, this design achieves an effective number of bits (ENOB) of 10.71 and a figure of merit (FoM) of 110.5 fJ/step. By interpolating a DCA‐TDC, the quantization speed is enhanced compared to traditional SS ADCs, presenting an effective solution for high‐frame‐rate CIS implementations. [ABSTRACT FROM AUTHOR]

Strength testing of concrete mainly relies on physical experiments, which are not only time-consuming but also costly. To solve this problem, machine learning has proven to be a promising technological tool in concrete strength prediction. In order to improve the accuracy of the model in predicting the compressive strength of concrete, this paper chooses to optimize the base learner of the ensemble learning model. The position update formula in the search phase of the sparrow search algorithm (SSA) is improved, and piecewise chaotic mapping and adaptive t-distribution variation are added, which enhances the diversity of the population and improves the algorithm's global search and convergence abilities. Subsequently, the effectiveness of the improvement strategy was demonstrated by comparing improved sparrow search algorithm (ISSA) with some commonly used intelligent optimization algorithms on 10 test functions. A back propagation neural network (BPNN) optimized with ISSA was used as the base learner, and the adaptive boosting (AdaBoost) algorithm was used to train and integrate multiple base learners, thus establishing an adaptive boosting algorithm based on back propagation neural network improved by the improved sparrow search algorithm (ISSA-BPNN-AdaBoost) concrete compressive strength prediction model. Then comparison experiments were conducted with other ensemble models and single models on two strength prediction datasets. The experimental results show that the ISSA-BPNN-AdaBoost model exhibits excellent results on both datasets and can accurately perform the prediction of concrete compressive strength, demonstrating the superiority of ensemble learning in predicting concrete compressive strength. [ABSTRACT FROM AUTHOR]

There are now ever-increasing amounts of digital data stored daily. Processing such big data requires new sciences such as data mining. Data mining aims to find the information and knowledge hidden in these massive amounts of data. Data mining has various branches and applications, one of which is clustering. In clustering, data are classified into several clusters. The data of one cluster are similar to each other but different from those of other clusters. Processing time is considered a major challenge, given the use of big data in data mining applications. Metaheuristic algorithms can solve large-scale time-consuming problems in acceptable durations. The harmony search is one of such algorithms. This paper proposes a hybrid method for data clustering by integrating the harmony search algorithm (HSA) with k-medoids, which is a clustering algorithm. Based on k-medoids, the proposed method uses real data samples as cluster centroids to eliminate the effects of noise data. Furthermore, the HSA reduces the data clustering duration. In addition, trees social relations optimization algorithm (TSR) was used to increase the quality of the solutions in the proposed method. The proposed method was compared practically with the other techniques. According to the comparison results, the proposed method outperformed similar techniques in terms of the solution fitness and Dunn and Silhouette criteria. The temporal outputs of the proposed method were also acceptable. The best results obtained for the proposed method in the form of fitness of the final solution is equal to 1.02, execution time is equal to 2.9562 s, Dunn index is equal to 0.067 and Silhouette is equal to 1. [ABSTRACT FROM AUTHOR]

The Near-Earth Asteroid Scout (NEA Scout) mission aimed to deliver a 6U CubeSat to a slow flyby of a Near Earth Asteroid using a solar sail as the primary propulsion system. NEA Scout was launched as a secondary payload aboard Artemis I, on November 16, 2022, but communication with the spacecraft was not achieved. This paper describes the target asteroid selection and the innovative trajectory and maneuver design for the NEA Scout mission, including cold gas maneuvers, multiple lunar flybys, solar sail thrusting, and design margins for solar sailing. The resulting trajectory design tools, solutions, and analyses are potentially applicable to future solar sail missions. • The pre-launch trajectory and maneuver design framework for NEA Scout is outlined. • Solar sail transfers are found to a Near-Earth asteroid from an Artemis 1 launch. • A broad search algorithm examines the entire catalog of Near-Earth asteroids. • Reference end-to-end trajectories for NEA Scout are computed automatically. • A new solar sail margin philosophy is also successfully applied to the trajectories. [ABSTRACT FROM AUTHOR]

Maximin distance designs have attracted increasing attention in computer experiments owing to their appealing space‐filling properties. The quality of these designs is typically evaluated by comparing their separation distance with the associated upper bound. Nevertheless, deriving tight upper bounds for the separation distance of designs remains a challenging problem that has been infrequently addressed in the literature. In this study, we obtain a new upper bound for the separation distance of certain classes of five‐level U‐type designs. We also investigate the characteristics of maximin distance U‐type designs and show the optimality of some existing orthogonal designs. Based on these theoretical results, we develop an efficient algorithm for searching maximin distance U‐type designs. Numerical studies and comparisons are given to show the superior performance of the obtained designs. [ABSTRACT FROM AUTHOR]

Jamming resource allocation is an important step in implementing cooperative jamming. Traditional jamming resource allocation considers only the reduction of enemy radar detection performance but ignores the problem of mutual interference in the formation. Therefore, an adaptive scheduling strategy based on the Improved Sparrow Search Algorithm (ISSA) is proposed to improve the jamming efficiency and minimize mutual interference. First, the target detection model and the mutual interference model are established. On this basis, a two-factor objective function based on network detection probability and the degree of mutual interference is constructed. Then, a whole-process cooperative mechanism is proposed, and an improved circle algorithm and a whale predation strategy are introduced. These strategies are used to overcome the problems of slow convergence and local optimization in the standard sparrow search algorithm (SSA). Finally, the effectiveness and timeliness of ISSA are proved by simulation experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid technique for optimizing Combined Cooling, Heating, and Power (CCHP) systems integrating a diverse set of components including geothermal heat pumps, gas turbines, absorption chillers/heaters, and photovoltaic/thermal collectors, along with storage units like batteries and water tanks. The innovation lies in combining the Capuchin Search Algorithm (CapSA) with the Wild Horse Optimizer (WHO) to concurrently optimize primary energy savings, cost reductions, and carbon dioxide emissions. Parametric distributions are used in probabilistic strategies to resolve uncertainty in solar radiation and building load. CapSA optimizes the CCHP system's design, while WHO enhances its performance. The effectiveness of the proposed strategy is demonstrated through implementation in MATLAB, where it achieves an average ACSR (Average Comprehensive Saving Rate) enhancement of 5.48%. This numerical result highlights a substantial improvement over existing methodologies, showcasing the superior performance of the hybrid optimization approach in enhancing energy efficiency and environmental sustainability of CCHP systems. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) play a significant role in monitoring changes in the physical environment, relying on a subset of designated nodes known as the Connected Dominating Set (CDS) to facilitate communication and serve as the network's virtual backbone for routing and clustering. Despite the various algorithms for CDS construction, the challenges persist in terms of construction cost and scalability. In this context, this paper introduces the utilisation of the Hunger Games Search Optimisation Algorithm (HGSOA) for Connected Dominating Set Construction in Wireless Sensor Networks (HGSOA-CDS-WSN). The primary intention of this paper is "to develop a technique capable of constructing small-sized CDS with reduced construction costs, particularly suitable for uniformly or randomly distributed nodes". The proposed HGSOA-CDS-WSN method is implemented in MATLAB based on various metrics. The results are compared with those of existing techniques, such as the Parallel Structure from Motion for UAV Pictures using Weighted CDS (PSfM-CDS-WSN), Polyhedra Associated with Locating-Dominating, Open Locating-Dominating, and Locating Total-Dominating Sets in Graphs (PAL-CDS-WSN), and Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for Internet of Things Networks (SHE-CDS-WSN). The proposed technique achieves lower delay by 13.24%, 25.64%, and 9.96%, and higher throughput by 18.34%, 24.55%, and 23.04% compared to the existing methods. This systematic evaluation underscores the efficacy and superiority of the proposed HGSOA-CDS-WSN technique in terms of connected dominating set size, construction time, energy efficiency, network stability, and overall performance, highlighting its potential to advance wireless sensor network applications. [ABSTRACT FROM AUTHOR]

The circular scan architecture (CSA) is a widely used scan design technique in digital circuit testing that involves scanning test data through a circuit in a circular pattern. Several optimization approaches have been suggested so far to lessen the Test Data Volume (TDV), but no one has shown satisfactory results. Therefore, this paper proposes a Modified and Customized Wingsuit Flying Search algorithm to decrease the test data volume time. The proposed MCWFS-SBA-CSA introduces some novel features that distinguish it from traditional circular scan approaches. The concept of wingsuit flying is an innovative approach in the field of scan systems. This allows the scan that moves more dynamically and efficiently, enabling it to cover a larger area in a shorter time than traditional circular-scan approaches. After this phase, only scan-chosen inputs are required to obtain the next test pattern from the captured response. Therefore, required less Automatic test equipment (ATE) memory in this research so there is a Decreased cost in system-on-chip (SOC). The proposed method attains fast global searching capacity. The efficiency of the proposed technique attains 26.42%, 32.45%, and 29.34% higher compression ratio (CR), Test Application Time (TAT) reduction of SC-128 attains 29.45%, 34.67%, and 40.22% higher, TDV of SC-256 attains 32.23%, 36.34%, and 39.23% higher than existing methods, such as TDV for Circular Scan Approaches utilizing Modified Shuffled Shepard Optimization (TDV-CSA-MSSO), Effectual Very-large-scale integration Test data compression system for CSA under modified and colony metaheuristic (ETDC-CSA-MACMH), and Test Data Compression Utilizing Tetrad state Skip System (TDC-DC-TSSC) for Digital Circuits, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a computational model for one kind of two-degree-of-freedom (2-DOF) upper limb exoskeleton robot. This robot consists of two connected body segments to imitate the upper shoulder and elbow joints of human hand. Two sets of direct current (DC) motors are severally used to drive the robotic shoulder and elbow joints. The dynamic of the DC motor is accurately developed to establish the relationship between the input current and the actuated force/torque. The PID controller is considered as a standard in technical and industrial applications, and selected to control the whole system in this paper. Therefore, the tuning of PID gains is extremely important, while utilizing optimization algorithms to reduce the tracking error is one approach of tuning PID gains. This paper presented the improved beetle antennae search (IBAS) algorithm to optimize the PID gains. The original BAS algorithm can achieve a wide search range, cost low time complexity and gain high search accuracy. Moreover, the proposed IBAS algorithm can realize more extensive search scope and more precise search compared with the original BAS algorithm. Finally, the IBAS algorithm is compared with the original BAS, PSO and GA algorithms on the basis of the optimization results. The comparison results demonstrated that the IBAS algorithm gains superior performance in addressing PID tuning problems. [ABSTRACT FROM AUTHOR]

Optimizing blasting parameters is of paramount importance in minimizing overbreak during tunneling. Hence, this paper proposed an intelligent approach, which integrates separate parameter optimization based on varying rock mass qualities, with the objective of reducing overbreak. This novel intelligent method constructs a comprehensive model with three distinct functions, which can provide precise overbreak prediction, analyze the mechanisms by which input parameters influence overbreak, and integrate feature importance into the blasting parameters optimization process. First, the hyperparameters of seven tree-based algorithms were optimized using the Sparrow Search Algorithm (SSA), and the best predictive model was selected by comparing various performance metrics. Then, the importance and influencing mechanisms of input features on overbreak were revealed through the utilization of the Shapley Additive Explanation. Subsequently, interactions among crucial parameters were investigated, and their design values were recommended. Finally, a novel parameter optimization method was employed to reduce overbreak, which combines the conclusions drawn from the importance analysis of input features with SSA through three key steps: modification of the initial position, expanding the search scope, and neighborhood perturbation. Compared to the unimproved methods, the proposed approach can significantly reduce post-blast overbreak areas in different sections of the tunnel by 12.8 % and 16.4 %, respectively. • Predicted blast overbreak based on deep learning algorithms. • Stabilized importance analysis results of input parameters. • Revealed influencing mechanisms of overbreak via SHAP. • Incorporated feature importance with parameter optimization. [ABSTRACT FROM AUTHOR]

With the advancement of technology, the collaboration of multiple unmanned aerial vehicles (multi-UAVs) is a general trend, both in military and civilian domains. Path planning is a crucial step for multi-UAV mission execution, it is a nonlinear problem with constraints. Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints. At the same time, robustness should be taken into account to ensure the reliable and safe operation of the UAVs. In this paper, a self-adaptive sparrow search algorithm (SSA), denoted as DRSSA, is presented. During optimization, a dynamic population strategy is used to allocate the searching effort between exploration and exploitation; a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range; a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums. The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation(CEC) 2017 benchmark suite. Furthermore, a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations. Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations. [ABSTRACT FROM AUTHOR]

Grover's search algorithm (GSA) offers quadratic speedup in searching unstructured databases but suffers from exponential circuit depth complexity. Here, we present two quantum circuits called HX and Ry layers for the searching problem. Remarkably, both circuits maintain a fixed circuit depth of two and one, respectively, irrespective of the number of qubits used. When the target element's position index is known, we prove that either circuit, combined with a single multi-controlled X gate, effectively amplifies the target element's probability to over 0.99 for any qubit number greater than seven. To search unknown databases, we use the depth-1 Ry layer as the ansatz in the Variational Quantum Search (VQS), whose efficacy is validated through numerical experiments on databases with up to 26 qubits. The VQS with the Ry layer exhibits an exponential advantage, in circuit depth, over the GSA for databases of up to 26 qubits. [ABSTRACT FROM AUTHOR]

While the development of quantum computers promises a myriad of advantages over their classical counterparts, care must be taken when designing algorithms that substitute a classical technique with a potentially advantageous quantum method. The probabilistic nature of many quantum algorithms may result in new behavior that could negatively impact the performance of the larger algorithm. The purpose of this work is to preserve the advantages of applying quantum search methods for generalized pattern search algorithms (GPSs) without violating the convergence criteria. It is well known that quantum search methods are able to reduce the expected number of oracle calls needed for finding the solution to a search problem from O (N) to O (N) However, the number of oracle calls needed to determine that no solution exists with certainty is exceedingly high and potentially infinite. In the case of GPS, this is a significant problem since overlooking a solution during an iteration will violate a needed assumption for convergence. Here, we overcome this problem by introducing the quantum improved point search (QIPS), a classical–quantum hybrid variant of the quantum search algorithm QSearch. QIPS retains the O (N) oracle query complexity of QSearch when a solution exists. However, it is able to determine when no solution exists, with certainty, using only O (N) oracle calls. [ABSTRACT FROM AUTHOR]

Grover's algorithm leverages quantum computing to efficiently locate solutions in unstructured search spaces, outperforming classical approaches. Since Grover's algorithm requires prior knowledge of the number of solutions (M) within a search space of size N, previous studies assume M is estimated beforehand and focus on identifying all solutions. Here, we propose a two-step process that integrates both the estimation of M and the discovery of the solutions, optimizing the interactions between the two steps. To enhance efficiency, the estimation step captures as many solutions as possible, leaving the discovery step to focus on the remaining ones. To ensure accuracy, the discovery step continues searching until the probability of finding additional solutions becomes sufficiently low. We implemented and evaluated our methods, showing that over 80% of solutions were found during the estimation phase, allowing the discovery phase to conclude earlier, while identifying over 99% of solutions on average. In theory, the process requires N M × log(M) Grover's iterations in the worst case, but in practice, it typically terminates after iterations proportional to N . We expect that our methods will be applicable to various search problems and inspire further research on efficiently finding all solutions. [ABSTRACT FROM AUTHOR]

This paper studies robot speed control decisions and the energy consumption objective in a cyclic robotic cell scheduling problem. The study considers a two-machine flow shop cell that produces multiple part types. A robot handles the parts between the machines and buffers. We assume that the energy consumption of the robot during a move is a convex function of its speed. The problem is to sequence the parts, construct a robot move sequence, and plan the robot's speed levels to minimize two conflicting objectives: cycle time and energy consumption of the robot. We propose mathematical models that find Pareto-efficient solutions to the problem. For large instances, we propose heuristic search algorithms. Numerical results show that integrating robot speed decisions into the scheduling problem leads to significant energy saving. [ABSTRACT FROM AUTHOR]

Autonomous platooning technology has been considered a promising solution for reducing costs in the trucking industry. In trucking networks, the operation of autonomous truck platoons is constrained by many factors such as network topology, travel distance, and demand distribution, indicating the importance of deciding when and where to form and decompose platoons. This study investigates a novel location problem for platoon formation center (PFC) in a trucking network, which plays an important role in platooning operations. PFCs are specifically constructed as infrastructures for the formation and decomposition of truck platoons. Semi-automated truck platoons traveling between PFCs save labor and fuel. Therefore, each origin–destination (OD) pair in the trucking network can choose a beneficial transportation route via PFCs. This study aims to find the optimal PFC location scheme and OD demand routing scheme to minimize the total cost, which includes PFC construction, driver labor, and truck fuel costs. Accordingly, a mixed-integer linear programming model is developed. To effectively solve large-scale problems in practical applications, a variable neighborhood search algorithm embedded with a heuristic routing allocation algorithm is designed. We construct 20 instances with different characteristics, such as the number and distribution of customer nodes and PFC candidate nodes; construction cost of PFC candidate nodes; and amount and distribution of OD demand. We evaluate the efficiency of the algorithm based on the experimental results. Furthermore, the benefits of the new transportation mode are quantified, and planners are provided with insights into PFC construction. [ABSTRACT FROM AUTHOR]

In this paper, we seek a new modification way to ensure the positiveness of the conjugate parameter and, based on the Dai-Yuan (DY) method in the vector setting, propose an associated family of conjugate gradient (CG) methods with guaranteed descent for solving unconstrained vector optimization problems. Several special members of the family are analyzed and the (sufficient) descent condition is established for them (in the vector sense). Under mild conditions, a general convergence result for the CG methods with specific parameters is presented, which, in particular, covers the global convergence of the aforementioned members. Furthermore, for the purpose of comparison, we then consider the direct extension versions of some Dai-Yuan type methods which are obtained by modifying the DY method of the scalar case. These vector extensions can retrieve the classical parameters in the scalar minimization case and their descent property and global convergence are also studied under mild assumptions. Finally, numerical experiments are given to illustrate the practical behavior of all proposed methods. [ABSTRACT FROM AUTHOR]

We are witnessing an increasing use of data-driven predictive models to inform decisions in high-stakes situations, from lending and hiring to university admissions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies. At the same time, individuals may use knowledge, gained by transparency, to invest effort strategically in order to maximize their chances of receiving a beneficial decision. Our goal is to find decision policies that are optimal in terms of utility in such a strategic setting. First, we characterize how strategic investment of effort by individuals leads to a change in the feature distribution. Using this characterization, we show that, in general, optimal decision policies are hard to find in polynomial time, and there are cases in which deterministic policies are suboptimal. Then, we demonstrate that, if the cost individuals pay to change their features satisfies a natural monotonicity assumption, we can narrow down the search for the optimal policy to a particular family of decision policies with a set of desirable properties, which allow for a highly effective polynomial time-heuristic search algorithm using dynamic programming. Finally, under no assumptions on the cost, we develop an iterative search algorithm that is guaranteed to converge to locally optimal decision policies. Experiments on synthetic and real credit card data illustrate our theoretical findings and show that the decision policies found by our algorithms achieve higher utility than those that do not account for strategic behavior. This paper was accepted by Vivek Farias, data science. Funding: This work was supported by Machine Learning Cluster of Excellence [EXC #2064/1, Project #390727645], H2020 European Research Council [Grant 945719], and Bundesministerium für Bildung und Forschung [Grant 01IS18039B]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02567 [ABSTRACT FROM AUTHOR]

Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global and local searches during optimization, the BES algorithm effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers a comprehensive review of recent research on BES. Beginning with an introduction to BES's natural inspiration and conceptual optimization framework, it explores modifications, hybridizations, and applications of BES across various domains. Then, a critical evaluation of BES's performance is provided, offering an update on its effectiveness compared to recently published algorithms. Furthermore, the paper presents a meta-analysis of BES developments and outlines potential future research directions. As swarm-inspired metaheuristic algorithms become increasingly important in tackling complex optimization problems, this study is a valuable resource for researchers aiming to understand swarm-based algorithms, mainly focusing on BES comprehensively. It investigates BES's evolution, exploring its potential applications in solving intricate optimization challenges across diverse fields. [ABSTRACT FROM AUTHOR]

Vehicle Routing Problem with Uncertain Demands (VRPUD) is one of the research hotspots in the field of logistics scheduling. In this paper, a Discrete Jaya (DJaya) algorithm is presented for the VRPUD to minimize the total cost. A novel dividing-point-based coding scheme is designed to represent solutions with higher robustness. In addition, an efficient repair strategy is embedded into the decoding process to avoid the failure of producing feasible solutions. The best and worst solutions are employed to generate offspring solutions in DJaya. Several efficient local search methods are also presented to enhance the exploitation ability and increase the diversity of solutions. Based on the benchmark data sets of the VRPUD, numerical simulations are carried out for the proposed DJaya algorithm with uncertain demands. Computational results and comparisons with the state-of-the-art algorithms demonstrate the superiority of the proposed algorithm in solving VRPUD. [ABSTRACT FROM AUTHOR]

The short-term power load forecasting provides an essential foundation for the dispatching management of the power system, which is crucial for enhancing economy and ensuring operational stability. To enhance the precision of the short-term power load forecasting, this paper proposes a hybrid prediction algorithm based on sparrow search algorithm (SSA), convolutional neural network (CNN) and long short-term memory (LSTM). First, feature datasets are constructed based on date information, meteorological data, similar days. The CNN performs effective feature extraction on the data and feeds the results into the LSTM for time series data analysis. Second, eight key parameters are optimized by SSA for improving the prediction precision of the CNN-LSTM prediction model. Simulation results show that the R2 of the proposed model exhibits a substantial enhancement in comparison to other models, reaching 0.9919 and presents a remarkable decrease in MAPE resulting in a value of 1.2%. Furthermore, RMSE and MAE have decreased to 1.17MW and 0.97MW respectively. Therefore, the proposed method has the ability to improve the prediction accuracy, due to the advantages in data mining of CNN, good time series data fitting ability of LSTM, and excellent optimization ability of SSA. [ABSTRACT FROM AUTHOR]

Stochastic configuration network (SCN) is a powerful prediction model whose performance is significantly influenced by the configuration of the network parameters. To improve the prediction accuracy of the network, a cooperative stochastic configuration network (CSCN) based on a novel differential evolutionary sparrow search algorithm (DESSA), termed as DESSA-CSCN, is proposed. In the CSCN, the number of hidden layer nodes is adaptively adjusted according to the number of iterations, and the parameters of hidden nodes are cooperatively optimized by using a population-based metaheuristic algorithm. A sparse matrix is introduced to mitigate parameter overfitting caused by the increased number of hidden layer nodes. During parameter optimization, the fitness function is constructed by using the supervision mechanism of the SCN, and the DESSA is utilized as the metaheuristic algorithm to update the weights and biases. In order to verify the effectiveness of the DESSA-CSCN, several simulation experiments have been conducted. The performance of the DESSA is evaluated by the CEC2017 test suit, and the simulation results show that the DESSA exhibits better convergence accuracy and can jump out of local optima more effectively than other algorithms. The performance of the DESSA-CSCN is evaluated by 4 datasets from KEEL, and the simulation results indicate that the DESSA-CSCN achieves better prediction accuracy and faster prediction speed than other models. [ABSTRACT FROM AUTHOR]