Search

This study addresses a system-level configuration selection problem for reconfigurable single part flow lines (R-SPFLs) with parallel identical flexible machines at each stage. The problem is to determine the number of stages, the number of machines of a certain type at each stage and the assignment of operations to each stage in order to satisfy the demand of a part type. Precedence relations among the operations and space limitations are also considered. For the objective of minimising the sum of machine purchase and operation processing costs, a nonlinear integer programming model is developed that gives the optimal solutions. Then, due to the problem's complexity, a basic variable neighbourhood search algorithm is proposed that constructs an initial solution using a greedy-type heuristic and improves it using a shaking and a local search improvement methods. Moreover, the basic algorithm is extended to a general one that uses a variable neighbourhood descent method. Computational results show that the two algorithms outperform the previous one significantly in both solution quality and computation times, and the general algorithm improves the basic one significantly. Finally, the applicability of the variable neighbourhood search algorithms is shown by reporting a brief case study. [ABSTRACT FROM AUTHOR]

In the automotive industry, some vehicles, failed vehicles, cannot be produced according to the planned schedule due to some reasons such as material shortage, paint failure, etc. These vehicles are pulled out of the sequence, potentially resulting in an increased work overload. On the other hand, the reinsertion of failed vehicles is executed dynamically as suitable positions occur. In case such positions do not occur enough, either the vehicles waiting for reinsertion accumulate or reinsertions are made to worse positions by sacrificing production efficiency. This study proposes a bi-objective two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. Moreover, an evolutionary optimisation algorithm, a two-stage local search algorithm, and a hybrid approach are developed. Numerical experiments over a case study show that while the hybrid algorithm better explores the Pareto front representation, the local search algorithm provides more reliable solutions regarding work overload objective. Finally, the results of the dynamic reinsertion simulations show that we can decrease the work overload by 20% while significantly decreasing the waiting time of the failed vehicles by considering vehicle failures and integrating the reinsertion process into the mixed-model sequencing problem. [ABSTRACT FROM AUTHOR]

The locomotive routing problem (LRP) considers the transportation of non-motorised containers called torpedo ladle cars (TPCs) within a steelworks. Locomotives are responsible for picking up and delivering TPCs to fulfil requests for various facilities, including steelmaking, ironmaking, heating, and repair facilities. This study introduces the many-to-many locomotive routing problem (M2MLRP), specifically tailored for the steel industry. M2MLRP was formulated using pattern- and routing-based models, and a logic-based Benders-decomposition-based matheuristic algorithm was developed. The pattern-based model successfully solved 20 small randomly generated instances, while the proposed matheuristic algorithm demonstrated the rapid generation of near-optimal solutions. Notably, unlike the pattern- and routing-based models, the proposed algorithm extended its capability to generate good feasible solutions for medium- and large-sized instances. Across all instances, the matheuristic algorithm outperformed both the routing-based model and an adaptive large neighbourhood search algorithm, showcasing its superior solution quality. This study provides a significant contribution to the field by being the first to address M2MLRP in the context of the steel industry. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel contour search algorithm (CSA) for image processing. Its efficacy is evaluated through a comparative analysis with established techniques such as Canny Edge and Snakes: Active contour models, utilizing infrared thermography (IRT) images. Based on the new CSA, we investigate the influence of different pulse shapes on the IRT post-processing methodology, particularly focusing on the thermal shock response spectrum (TSRS), using two different heat sources: xenon flash lamps and a laser. Moreover, this allows for a more precise quantitative comparison of the TSRS with existing IRT post-processing techniques, including pulse phase thermography and thermal signal reconstruction, concerning the detection of defects in composite materials, particularly in carbon fiber-reinforced polymer. A quantitative comparison was performed using the Tanimoto criterion and signal-to-noise ratio. A more detailed analysis is conducted to identify inherent limitations and potential benefits of the new TSRS methodology. We further investigate and experimentally confirm our previous finding on the qualitative correlation between the one-dimensional thermal N-layer model and test data from the TSRS optimization process for defect determination. This correlation can eliminate the time-consuming optimization step, making TSRS a more attractive alternative to common IRT methods and enhancing the quantitative description of defects. [ABSTRACT FROM AUTHOR]

Due to the severity of resource consumption and uncertainty in orders, new challenges have arisen for production scheduling in enterprises. This paper studies the scheduling problem of minimising the total weighted tardiness for jobs with fuzzy processing times and due dates on identical parallel machines with resource constraint. To address this research problem, we first propose methods to calculate the upper bound of resource consumption and the maximum number of machines that can be used, effectively reducing the search space and improving the algorithm's efficiency. Secondly, a local search algorithm based on job swapping is proposed to enhance the algorithm's performance. Then, repair algorithms based on job removal and job swapping are designed to repair infeasible solutions. Finally, we propose a fuzzy genetic algorithm (FGALS) to solve the problem based on the above elements. Through extensive simulation experiments, the effectiveness and efficiency of the FGALS algorithm are verified by comparing it with commercial solver Gurobi and several meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Colloids can acquire a dipolar interaction in the presence of an external AC electric field. At high field strength, the particles form strings in the field direction. However, at weaker field strength, competition with isotropic interactions is expected. One means to investigate this interplay between dipolar and isotropic interactions is to consider clusters of such particles. Therefore, we have identified, using the GMIN basin-hopping tool, a rich library of lowest energy clusters of a dipolar colloidal system, where the dipole orientation is fixed to lie along the z axis and the dipole strength is varied for m-membered clusters of 7 ≤ m ≤ 13. In the regime where the isotropic and dipolar interactions are comparable, we find elongated polytetrahedral, octahedral, and spiral clusters as well as a set of non-rigid clusters, which emerge close to the transition to strings. We further implement a search algorithm that identifies these minimum energy clusters in bulk systems using the topological cluster classification [J. Chem. Phys. 139 234506 (2013)]. We demonstrate this methodology with computer simulations, which show instances of these clusters as a function of dipole strength. [ABSTRACT FROM AUTHOR]

Balancing conflicting goals among different stakeholders is a challenging problem in various application domains. In this paper, we analyse it in the context of home healthcare. Fairness objective functions for nurses and patients are combined with system-level governance goals of the territorial centers in charge of the assistance service. From the solution of several multi-objective problems including one objective for each actor hierarchically ordered, the best goal for each stakeholder is identified and interesting managerial conclusions are drawn. To efficiently solve large-size instances, we introduce a parallel Adaptive Large Neighborhood Search algorithm with destroy and repair operators customised to solve multi-objective multi-actor problems. The algorithm proves to be highly efficient and effective when compared to a commercial Mixed Integer Programming solver, either in its plain form or enforced by a mild start and a primal heuristic. Additionally, we devise a metaheuristic method to generate the Pareto frontier of an instance. [ABSTRACT FROM AUTHOR]

Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial and non-convex problems. For example, they are the foremost method for solving (mixed) integer programs and constraint satisfaction problems. Tree search algorithms come with a variety of tunable parameters that are notoriously challenging to tune by hand. A growing body of research has demonstrated the power of using a data-driven approach to automatically optimize the parameters of tree search algorithms. These techniques use a training set of integer programs sampled from an application-specific instance distribution to find a parameter setting that has strong average performance over the training set. However, with too few samples, a parameter setting may have strong average performance on the training set but poor expected performance on future integer programs from the same application. Our main contribution is to provide the first sample complexity guarantees for tree search parameter tuning. These guarantees bound the number of samples sufficient to ensure that the average performance of tree search over the samples nearly matches its future expected performance on the unknown instance distribution. In particular, the parameters we analyze weight scoring rules used for variable selection. Proving these guarantees is challenging because tree size is a volatile function of these parameters: we prove that, for any discretization (uniform or not) of the parameter space, there exists a distribution over integer programs such that every parameter setting in the discretization results in a tree with exponential expected size, yet there exist parameter settings between the discretized points that result in trees of constant size. In addition, we provide data-dependent guarantees that depend on the volatility of these tree-size functions: our guarantees improve if the tree-size functions can be well approximated by simpler functions. Finally, via experiments, we illustrate that learning an optimal weighting of scoring rules reduces tree size. [ABSTRACT FROM AUTHOR]

Human–robot collaborative technology maximises the advantages of the capabilities of humans and robots, and provides diverse operating scenarios for the remanufacturing industry. Accordingly, this paper proposes an innovative human–robot collaborative disassembly line balancing problem (HRC-DLBP). First, a mixed-integer programming (MIP) model is devised for the HRC-DLBP to minimise the number of workstations, smoothness index, and various costs. Second, a hybrid local search genetic algorithm (HLSGA) is developed to solve the proposed HRC-DLBP efficiently. According to the problem characteristics, a four-layer encoding and decoding strategy was constructed. The search mechanism of the local search operator was improved, and its search strategy was adjusted to suit the genetic algorithm structure better. Furthermore, the accuracy of the proposed MIP model and HLSGA is verified through two HRC-DLBP examples. Subsequently, three HRC-DLBP examples are used to prove that the HLSGA is superior to five other excellent algorithms. The case of the two-sided disassembly line problem reported in the literature is also solved using the HLSGA. The results are found to be significantly better than the reported outputs of the improved whale optimisation algorithm. Besides, HLSGA also outperforms the results reported in the literature in solving EOL state-oriented DLBP. Finally, the HLSGA is applied to a power battery disassembly problem, and several optimal allocation schemes are obtained. [ABSTRACT FROM AUTHOR]

Combined heat and power economic dispatch (CHPED) is one of the foremost subjects in the operation of power systems. In this article, a new variant of cuckoo search (CS) algorithm, elitist CS (yECS), is advanced to tackle CHPED. During the optimisation process of the original CS as well as lots of its variants, the guidance of search directions relies merely upon the best individual, causing the loss of other beneficial information and further influencing their performance potentials. Therefore, in yECS, an elitist mechanism is developed to fully utilise the beneficial information of other elite individuals. Specifically, three new iterative strategies are developed, one for the global search phase and the other two for the local search phase. Further, a coordinated mechanism is put forward to effectively integrate the local iterative strategies, thus helping yECS in maintaining an appropriate balance between exploitation and exploration. The superior performance of yECS is firstly substantiated via CEC 2017 test suite and two engineering design problems and then it is utilised to address CHPED problems. All optimal dispatch results acquired by yECS are feasible and in most cases display remarkable improvements over the results determined by other CS variants and some recently-published literature results. [ABSTRACT FROM AUTHOR]

Since the beginning of COVID-19, more than 13,036,550 people have been infected, and 571,574 died because of the disease by July 13, 2020. Developing new methodologies to predict the COVID-19 pandemic will help policymakers plan to contain the spread of the virus. In this research, we develop a Stochastic Fractal Search algorithm combined with a mathematical model to forecast the pandemic. To enhance the algorithm, we employed a design of the experiments approach for tuning.We applied our algorithm to public datasets to model the COVID-19 pandemic in Canada in the upcoming months. Our algorithm predicts the number of symptomatic, asymptomatic, life-threatening, recovered, and death cases. The outcomes reveal that asymptomatic cases play the main role in the transmission of the virus.Wealso show that increasing the testing capacity would enhance the detection of asymptomatic cases and limit community transmission. Moreover, we performed sensitivity analyses to discover the effects of changes in transmission rates on pandemic growth. The sensitivity analyses provide a realistic overview of the future number of cases if the transmission rates change due to the emergence of new variants or change in social measures. Considering the outcomes, we provide several managerial insights to minimize community transmission. [ABSTRACT FROM AUTHOR]

Exact conditions have long been used to guide the construction of density functional approximations. However, hundreds of empirical-based approximations tailored for chemistry are in use, of which many neglect these conditions in their design. We analyze well-known conditions and revive several obscure ones. Two crucial distinctions are drawn: that between necessary and sufficient conditions and that between all electronic densities and the subset of realistic Coulombic ground states. Simple search algorithms find that many empirical approximations satisfy many exact conditions for realistic densities and non-empirical approximations satisfy even more conditions than those enforced in their construction. The role of exact conditions in developing approximations is revisited. [ABSTRACT FROM AUTHOR]

This paper delves into the multifaceted role of search algorithms in the digital landscape. While these algorithms do enhance efficiency and personalization of searching, ethical concerns arise due to inherent biases, which reinforce stereotypes and adversely affect individuals of color, women, queer people, and other minority communities. A proposed solution includes creating an external consulting-like company that can discover and quantify these biases. The biggest challenge lies in then using this to make changes within tech companies, given limited financial incentives, a lack of diversity in algorithm-building teams, and industry monopolies. To address these issues, an adjoining legal team specializing in internet anti-discrimination policy could work to set legal precedents that drive ethical changes. The discussion encompasses the organization’s viability within a capitalist society, and concludes by highlighting the broader implications of algorithmic biases across various domains, emphasizing the need for immediate, creative, and interdisciplinary solutions to address social justice issues in algorithmic decision-making.

This paper presents a bi-objective solution approach to address the production scheduling challenge encountered by manufacturers in a shared manufacturing environment. In such scenarios, manufacturers are required to manage orders received through a cloud platform (referred to as cloud orders) while simultaneously fulfilling orders from their long-term and regular clients (local orders). The problem is to efficiently coordinate the production of both types of orders within shared manufacturing facilities. We formulate the problem into a bi-objective mixed integer programming model aimed at simultaneously minimizing the delivery time of cloud orders and mitigating the disruptions to local order production caused by cloud orders. This solution approach comprises three key components: computation of cloud orders' starting times, construction of available time intervals of manufacturing facilities, and a bi-objective heuristic. This heuristic combines an enhanced hybrid discrete differential evolution with a modified forward–backward earliest starting time algorithm. We introduce an advanced population initialization technique, a novel individual update strategy, and an adaptive local search mechanism based on Pareto-dominance principles to improve the search capabilities of the algorithm towards discovering Pareto non-dominated solutions. Computational results show that the proposed approach outperforms the existing algorithm in most test instances in terms of five common metrics. Insights are discussed, highlighting the practical implications and potential benefits of the proposed approach for shared manufacturing scheduling. [ABSTRACT FROM AUTHOR]

Most engineered systems intend to perform missions with a pre-specified target success probability to reduce undesirable failure risks. Before executing the next mission, inspection activities are conducted across various physical levels for assessing the probability of mission success. However, due to the randomness of a system's degradation behavior and the presence of measurement errors, inspection results inevitably contain uncertainty. Meanwhile, mission durations and acceptable system states may also be uncertain, due to uncontrollable factors, such as random operating environments and mission demands. In such a circumstance, it is of great significance to identify the optimal multilevel inspection policy to answer, as great confident as possible, the question that the system can complete the next mission with a target mission success probability. This paper develops a novel metric to gauge the effectiveness of a multilevel inspection policy to assess if the system can complete the next mission with a pre-specified target success probability from a risk-averse perspective, based on which an optimization method is proposed to seek an inspection policy under uncertain scenarios with the aim of minimizing the maximum regret of the proposed metric. A stochastic fractal search algorithm, along with two tailored local search rules, is designed to efficiently resolve the resulting optimization problem. Two cases, including a three-component system and a rocket fueling mechanism's control system, are used to illustrate the efficacy of the proposed approach, which is capable of effectively identifying the risk of mission failures by inspection policies. [ABSTRACT FROM AUTHOR]

As typical frame rates allow limited exposure time, camera-captured videos under low-light conditions often suffer from poor contrast and noise. Existing models failed to consider dark and light areas' boundary pixels and varying low-illuminated night videos' weather conditions for removing noise and enhancing contrast. Hence, the video's visual appearance under low-light is improved using BFR-SEQT. Primarily, a video is inputted and converted into frames. Also, colour space is converted from which static and dynamic pixels are detected regarding frame differences. Using LoG-MF and KF algorithms, noise is removed from which foreground and background are separated using SD-FCM. Motion is estimated and features are extracted to enhance contrast. Then, pixel grouping using LC-GOA is done. Lastly, enhanced outputs from both phases are reconstructed and enhanced video is obtained. The proposed model improves video quality by enhancing contrast and removing noise with high PSNR values (27.6589db and 24.5478db), thus outperforming conventional methods. [ABSTRACT FROM AUTHOR]

The A* search algorithm is widely utilized to evaluate the shortest path in a given network. However, in a traditional A* search algorithm, the nodes are assumed to have crisp values, i.e., a single value. This assumption may not hold in many real-world scenarios where uncertainty or ambiguity is involved. In such cases, an interval-valued Neutrosophic Pythagorean (IVNP) environment can provide a more sound and accurate representation. Interval-valued Neutrosophic Pythagorean sets (IVNPS) are an effective way to model vague and imprecise data, which is prevalent in executive problems. These sets provide a more flexible way to capture uncertainty by allowing the values of nodes in the graph to vary within certain intervals rather than having fixed values. This interval representation can effectively handle imprecise or incomplete information and is a powerful tool in executive processes. In this research paper, we proposed an improved A* search algorithm that takes advantage of the interval-valued neutrosophic Pythagorean environment. This algorithm aims to evaluate the shortest path in a graph under uncertainty and ambiguity. The proposed algorithm incorporates the IVNPS theory into the A* search framework to handle the uncertainty in node values and edge weights. It utilizes the concept of neutrosophic Pythagorean distance to calculate the heuristic function and make informed decisions on the next node to expand. [ABSTRACT FROM AUTHOR]

Developing effective soil moisture estimating systems can provide substantial information for various applications including precision agriculture and ecosystem management. This highlights the need to use data mining and machine learning algorithms to estimate soil moisture accurately. For this purpose, an efficient backtracking search-based extreme gradient boosting algorithm (BS-XGB) algorithm is presented for soil moisture estimation. The incentive mechanism of the proposed BS-XGB is tuning the hyper-parameters of the extreme gradient boosting optimally by incorporating the backtracking search algorithm, which significantly improves the prediction performance. The proposed algorithm is evaluated on a benchmark dataset containing daily soil moisture parameters in four depths 10, 25, 50, and 100 cm sampled from the Kingston station in the United States of America. The results indicated that the BS-XGB model achieved impressive performance in estimating soil moisture, with an R² of 0.999 for the training dataset and an R² of 0.973 for the testing dataset. Comparing the results of BS-XGB with those of the counterpart algorithms proved its superiority in terms of statistical metrics. The feature importance analysis suggested that the variables of soil temperature, relative humidity, minimum temperature, and solar radiation are the most important factors in soil moisture prediction. The results reveal that the proposed model with a high degree of confidence can be used as a qualified alternative to predict soil moisture and save time and cost. [ABSTRACT FROM AUTHOR]

In order to improve the global search ability of the optimization algorithm when solving the path synthesis problem, this paper proposes a synthesis method of a four-bar mechanism based on the bisection strategy of the Lévy-PSO evolutionary algorithm. First, a synthesis equation of the target point is established based on the closed-loop vector method. Use the bisection strategy to cut the hinge coordinate variation range to reduce the probability of premature maturity of the algorithm. The error algorithm of the circular proximity function is used to reduce the search dimension of the optimization algorithm and improve search efficiency. Use the improved particle swarm algorithm based on Lévy to improve global search capabilities. Four multi-point path synthesis problem examples are given to verify the effectiveness and accuracy of the method. [ABSTRACT FROM AUTHOR]

For numerous years, PVC and aluminum doors and windows have been integral components of buildings. The primary technical requisites for doors and windows encompass superior acoustic performance, thermal insulation, and effective sealing. Particularly, the sealing properties, crucially important, are ensured by the gaskets fitted onto the profiles. This paper aims to present a gasket design with a new methodology that includes a systematic modeling-optimization process based on multiple non-linear neuro-regression. Comprehensive research has been done by multiple non-linear neuro-regression analyses of gaskets due to deficient studies in the literature on hyperelastic materials. 17 candidate functional forms based on four basic mathematical structures (polynomial, trigonometric, logarithmic, and linear functions, and their hybrid or rational forms) have been proposed to model the FEM data of the gasket. Hybrid versions of three stochastic search algorithms, Differential Evolution, Random Search, and Simulated Annealing, are considered to solve highly non-linear, non-convex, multi-variables mixed continuous-discrete problems. The experimental results were compared with the numerical result using the finite-element analysis (FEA). Nonlinear static analyses were performed with Abaqus. Stress and logarithmic strain distributions are obtained. A good correlation was found between the experimental and the numerical results The optimum locations and numbers of the nodes have been found to obtain the minimum overflow distance and the maximum compression surface area for gaskets having the same topological structure. [ABSTRACT FROM AUTHOR]

In the realm of inventory management, the role of substitution in managing growing items is crucial. While several studies have examined growing and substitutable items separately, none have addressed replenishment policies for inventory systems with substitutable growing items. This paper introduces a multi-item inventory management problem for growing items with potential demand substitution. Two cases are defined and mathematically modeled. Due to the nonlinearity of the constrained models, a grid search heuristic algorithm is proposed as the solution methodology. The algorithm's performance is compared with genetic and simulated annealing algorithms, two state-of-the-art metaheuristics. The models and solution approach are evaluated through numerous numerical examples, demonstrating the grid search heuristic algorithm's superiority in quality and computational time. Sensitivity analyses are conducted to assess the impact of variations in input parameters on the objective function. Managerial insights are derived from the results, and the paper concludes with directions for future research on both the problem and solution methodology. [ABSTRACT FROM AUTHOR]

The Cross-domain Heuristic Search Challenge (CHeSC) is a competition focused on creating efficient search algorithms adaptable to diverse problem domains. Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process. Numerous selection hyper-heuristics have different implementation strategies. However, comparisons between them are lacking in the literature, and previous works have not highlighted the beneficial and detrimental implementation methods of different components. The question is how to effectively employ them to produce an efficient search heuristic. Furthermore, the algorithms that competed in the inaugural CHeSC have not been collectively reviewed. This work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance. A summary of the main characteristics and classification of the algorithms is presented. The analysis underlines efficient and inefficient methods in eight key components, including search points, search phases, heuristic selection, move acceptance, feedback, Tabu mechanism, restart mechanism, and low-level heuristic parameter control. This review analyzes the components referencing the competition's final leaderboard and discusses future research directions for these components. The effective approaches, identified as having the highest quality index, are mixed search point, iterated search phases, relay hybridization selection, threshold acceptance, mixed learning, Tabu heuristics, stochastic restart, and dynamic parameters. Findings are also compared with recent trends in hyper-heuristics. This work enhances the understanding of selection hyper-heuristics, offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains. [ABSTRACT FROM AUTHOR]

Purpose: Exact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently. Design/methodology/approach: Minimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima. Findings: The developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005. Research limitations/implications: In this study, TCTP is solved without considering quality or restrictions on the resources. Originality/value: The proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel derivative-free projection algorithm based on the classical Polak–Ribiére–Polyak (PRP) method. This algorithm designs a search direction with sufficient descent and trust region properties, integrating an efficient line search approach and projection techniques. Under standard assumptions, the global convergence of the proposed algorithm is established. Numerical experiments demonstrate that the proposed algorithm outperforms two existing algorithms in terms of efficiency and robustness, and is successfully applied to sparse signal recovery and image denoising problems. [ABSTRACT FROM AUTHOR]

Due to the high dependency of photovoltaic (PV) solar cell's output on solar irradiance and, the ambient temperature. Maximum power point tracking (MPPT) algorithms are used extensively to operate the system at its full potentials. Moreover, being installed in outdoor spaces, PV modules are inevitably subjected to partial shading conditions, where different parts of the system are receiving different amounts of solar irradiance. In case of occurrence of partial shading conditions on a PV module that is equipped with bypass diodes, the power-voltage (P-V) curve will have multiple peaks. This multi-peak curve requires using an advanced algorithm which track the global maximum power point (GMPP) instead of being deceived and trapped in a local maximum power point. In this paper, the flamingo search algorithm (FSA) is adapted for GMPP tracking for a PV system under partial shading conditions. The FSA algorithm fetch for the GMPP by reading the PV panel power and setting accordingly the duty cycle of the buck converter. To investigate model validity, simulation is performed using the MATLAB/Simulink platform and results demonstrate good tracking performance and fast response that prove the robustness of the system against rapid variations in solar irradiance levels. [ABSTRACT FROM AUTHOR]

Sudoku is a puzzle that has a unique solution. No matter how many methods are used, the result will always be the same. The player thought that the number of givens or clues, the initial value on the Sudoku puzzles, would significantly determine the difficulty level, which is not necessarily correct. This research uses two search algorithms, breadth-first search (BFS) and depth-first search (DFS), to solve a set of Sudoku puzzles based on the number of givens. The Sudoku puzzles are chosen based on the number of givens between 32 and 35. In cases where Sudoku puzzles are considered medium or intermediate difficulty, the solutions generated by both algorithms will be compared with the human-solving approach. The research aims to determine whether humans tend to solve Sudoku puzzles with solutions resembling those generated by BFS or DFS. Furthermore, if all three approaches-human, BFS, DFS-yield comparable solutions, the Sudoku puzzle has only one unique solution. [ABSTRACT FROM AUTHOR]

Recently, electric vehicles have been utilized more often in city logistics for on-demand delivery due to either the financial subsidies from the government or the restrictions of some environmental policies. Among the various types of electric vehicles, electric mopeds play a significant role in the last-mile delivery of city logistics, especially for meal delivery scenarios. However, the limited driving range due to the small battery capacity complicates the application and scheduling of electric mopeds. In this research, we propose a battery-swapping station location-routing problem for the application of electric mopeds in meal delivery scenarios. The purpose is to determine the optimal deployment for battery-swapping stations and the routes for each rider simultaneously. Moreover, the scales of battery-swapping stations are also considered in terms of the number of available batteries. An improved adaptive large neighborhood search algorithm is devised to solve this problem, in which several customized operators are designed to facilitate the convergence of the searching process. Computational experiments demonstrate that the proposed algorithm outperforms the commercial solver Gurobi over small-scale instances in terms of the solving time and has a relatively dominant performance over the other two metaheuristic approaches for solving large instances. Furthermore, extensive sensitivity analysis is conducted regarding the cost settings of battery-swapping stations, the driving range of electric mopeds, and customer demand levels so as to provide practical and valuable managerial insights for city logistics enterprises. [ABSTRACT FROM AUTHOR]

To address the shortcomings of rural traditional truck logistics services, such as high distribution costs and low distribution efficiency, we propose a novel truck-drone-collection station collaborative delivery problem (TDCS-CDP). The integer programming model of truck-drone-collection station collaborative delivery is constructed, considering the rural road conditions and the geographical distribution of customers. Decisions are made to determine the routes for the truck and drone, as well as the locations and customer assignments for collection stations, drone launch and recovery nodes. The objective of the TDCS-CDP is to minimize the costs associated with truck-drone travel and collection station operations. To solve this complex problem, we propose an adaptive large multiple neighborhood search (ALMNS) algorithm that explores the solution space using multiple destruction-repair operators and neighborhood operators designed based on the problem structure. At the same time, the iteration period is segmented, and the weight information of the operators in each period is adjusted dynamically, which effectively prevents the algorithm from falling into local optimization. Finally, we conducted extensive computational experiments on a set of instances based on existing benchmarks. Out of 75 instances, ALMNS and Gurobi find 14 and 19 optimal solutions within a given time. However, the average computation time of ALMNS is about 2.76 % of Gurobi. The state-of-the-art algorithm obtains 29 best solutions out of 75 instances, while ALMNS achieves 75 best solutions in less computation time. [ABSTRACT FROM AUTHOR]

In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data. The methodology initially employs Variational Mode Decomposition (VMD) to preprocess and decompose the existing data from the target sensor into Intrinsic Mode Functions (IMFs) and residuals. Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. During this process, Singular Spectrum Analysis (SSA) is used to optimize the hyperparameters of the GRU network. To validate the effectiveness of this method, we utilized one month of monitoring data collected from a certain project and a publicly available dataset. On the public dataset, we tested performance at different data loss rates. Results show that, compared to a standalone GRU model and a VMD + GRU model, the VMD + SSA + GRU model's reconstruction data root mean squared error is reduced by 46.61% and 32.57% on average, respectively, while the coefficient of determination increases by 38.74% and 18.50%. The data reconstruction method proposed in this study can accurately capture trends in missing data, without the need for manual hyperparameter tuning, and the reconstruction results are highly consistent with the real data. [ABSTRACT FROM AUTHOR]

The security of IoT networks has become a major concern with the ubiquity of Internet of Things (IoT) technology. The concept of intrusion detection system (IDS) is a complex system to discover an intruder in the IoT platform, where the intruder can be a host that tries to gain some other nodes without authorization. Due to the complexity and resource constraints, classical IDS have their limitations in the context of IoT networks. There has been significant research in integrating both IDS and blockchain (BC) to detect existing and emerging cyberattacks and improve data privacy, correspondingly. In these approaches, learning-based ensemble algorithms can concurrently ensure data privacy and facilitate the detection of sophisticated malicious events. This study introduces a BC with fractal chaotic oppositional barnacles mating optimizer-based deep learning (BCOBMO-DL) Model for Secure IoT environment. The BCOBMO-DL technique exploits BC technology with DL-based intrusion detection to protect the IoT environment. In the BCOBMO-DL technique, the linear scaling normalization (LSN) approach can be used to scale the input data into a uniform format. In addition, the BCOBMO-DL technique designs the COBMO algorithm for electing the optimal subset of features. For intrusion detection, the self-attention bidirectional gated recurrent unit (SA-BiGRU) model is applied with fractal theory. Finally, the reptile search algorithm (RSA)-based hyperparameter tuning process is utilized for tuning the parameters based on the SA-BiGRU model. Furthermore, the BC technology is useful for achieving security in the IoT network. The experimental evaluation of the BCOBMO-DL method takes place on the benchmark NSLKDD dataset. The widespread experimental outcomes highlighted the enhanced detection outcomes of the BCOBMO-DL algorithm over other models. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a dementia type, which is highly faced by elderly persons. In AD, cells of the brain that are accountable for forming memories, as well as cognitive decisions, are affected, which forms shrinkage of the entire gray matter in the human brain. As the patients of with AD are increasing across the globe, researchers are attempting to devise an accurate method to diagnose the disease utilizing images of the brain. Here, Manta Crow Light Spectrum Optimization-based SpinalNet (MCLSO-SpinalNet) is designed for the classification of AD. The adaptive median filter is utilized for pre-processing, wherein Region of Interest (ROI) extraction is also performed. Thereafter, the segmentation process is performed utilizing K-Net and training of K-Net is done by Light Spectrum Optimizer (LSO). After that, features are extracted from the feature extraction stage. From extracted features, the detection of AD is accomplished by employing a Deep Neuro Fuzzy Network (DNFN) that is tuned by MCLSO. However, MCLSO is an incorporation of Manta Crow Search Optimization (MCSO) and LSO. Additionally, MCSO is designed by integrating Manta-Ray Foraging Optimization (MRFO) with the Crow Search Algorithm (CSA). At last, AD classification is executed with SpinalNet, which is also trained by MCLSO. Furthermore, MCLSO-SpinalNet obtained high accuracy, sensitivity, F-measure, and specificity of about 92.6%, 93.6%, 91.7%, and 93.3%. [ABSTRACT FROM AUTHOR]

In urban logistics distribution, vehicle carbon emissions during the distribution process significantly contribute to environmental pollution. While developing green logistics is critical for the sustainable growth of the logistics industry, existing studies often overlook the potential benefits of depot sharing among enterprises. By enabling depots belonging to different enterprises to be shared, it would shorten the distance traveled by vehicles returning to depots and reduce carbon emissions. And it would also reduce the number of depots being built. Therefore, a green vehicle routing problem with depot sharing is presented in the paper. To solve this problem, an improved adaptive large neighborhood search algorithm is presented, in which the Split strategy and two new operators are proposed to enhance solution quality and computational efficiency. Extensive numerical experiments are conducted on instances of varying scales to evaluate this algorithm, and also demonstrate its effectiveness and efficiency. Furthermore, the experimental results demonstrate that depot sharing significantly reduces carbon emissions, achieving an average optimization rate of 10.1% across all instances compared to returning to the original depot. [ABSTRACT FROM AUTHOR]

Elliptic curves with the special quality known as supersingularity have gained much popularity in the rapidly developing field of cryptography. The conventional method of employing random search is quite ineffective in finding these curves. This paper analyzes the search of supersingular elliptic curves in the space of curves over F p 2 . We show that naive random search is unsuitable to easily find any supersingular elliptic curves when the space size is greater than 10 13 . We improve the random search using a necessary condition for supersingularity. As our main result, we define for the first time an objective function to measure the supersingularity in ordinary curves, and we apply local search and a genetic algorithm using that function. The study not only finds these supersingular elliptic curves but also investigates possible uses for them. These curves were used to create cycles inside the isogeny graph in one particular application. The research shows how the design of S-boxes may strategically use these supersingular elliptic curves. The key components of replacement, which is a fundamental step in the encryption process that shuffles and encrypts the data inside images, are S-boxes. This work represents a major advancement in effectively identifying these useful elliptic curves, eventually leading to their wider application and influence in the rapidly expanding field of cryptography. [ABSTRACT FROM AUTHOR]

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs' coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard Crow Search Algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated. [ABSTRACT FROM AUTHOR]

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

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

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry. [ABSTRACT FROM AUTHOR]

Compared to fossil fuel-based internal combustion vehicles, electric vehicles with lower local pollution and noise are becoming more and more popular in urban logistic distribution. When electric vehicles are involved, high-quality delivery depends on energy consumption. This research proposes an electric vehicle routing problem considering time windows under energy consumption uncertainty. A mixed-integer programming model is established. The robust optimization method is adopted to deal with the uncertainty. Based on the modification of adaptive large neighborhood search algorithm, a metaheuristic procedure, called novel hybrid adaptive large neighborhood search, is designed to solve the problem, and some new operators are proposed. The numerical experiments show that the proposed metaheuristic can obtain high-performance solutions with high efficiency for large-scale instances. Furthermore, the robust solution based on the proposed model can achieve a satisfactory tradeoff between performance and risk. [ABSTRACT FROM AUTHOR]

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP's quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. [ABSTRACT FROM AUTHOR]

Accurately determining the mechanical parameters of surrounding rock in tunnel design and construction presents a significant challenge due to the complexity of the environment. This study proposes a novel approach for inverting these parameters using an advanced optimization method, the Improved Grey Wolf Optimization (IGWO), integrated with a BP neural network (IGWO-BP). Key enhancements such as cubic chaotic mapping, refraction backward learning, nonlinear convergence factors, and updated position formulas were applied to improve the algorithm's search efficiency. By optimizing the neural network's weights and biases, a precise relationship between rock mechanics and displacement was established. The method was validated through a case study of the Lianhua Tunnel (YK37 + 330 section), utilizing field data of crown settlement and peripheral displacement. The approach accurately predicted mechanical parameters, with relative errors below 5.02% for crown settlement and 4.15% for peripheral displacement. These results demonstrate the reliability and practical applicability of the proposed technique for tunnel engineering. [ABSTRACT FROM AUTHOR]

Regular monitoring of crop moisture conditions has the potential to enhance both crop production efficiency and water utilization efficiency. To generate a moisture status map using Unmanned Aerial Vehicle (UAV) visible light imagery, the focus of the present study was on developing a predictive model for cotton leaf water potential utilizing UAV visible light imagery and multi-source data. By processing the R, G, and B channel values of UAV visible light images, the grayscale histogram mean was extracted as an indicator of vegetation growth status. The Extreme Learning Machine (ELM) was employed in conjunction with the Sparrow Search Algorithm (SSA) optimization to create the SSA-ELM model. This model integrates the grayscale histogram method with soil data and meteorological data, and trains with leaf water potential as the output feature, establishing a cotton leaf water potential prediction model. The SSA-optimized model exhibited significantly improved performance on two test sets in Alaer and Tumushuke. After SSA optimization, the R2 values increased by 0.05 and 0.04 respectively, reaching 0.80 and 0.85, while the RMSE values were 0.211 MPa and 0.215 MPa, indicating the accuracy and stability of the model predictions. By utilizing data partitioned into three distinct cotton growth stages, the model was remodeled, followed by assessment using test sets from the same stages in the cities of Alaer and Tumushuke in 2023. On the Flowering test set, the R2 values were 0.70 and 0.71, with corresponding RMSE values of 0.272 MPa and 0.269 MPa, respectively. For the Full boll test set, the R2 values were 0.78 and 0.76, with corresponding RMSE values of 0.247 MPa and 0.245 MPa. Lastly, within the Open boll stage test group, the R2 values reached 0.75 and 0.80, with corresponding RMSE values of 0.240 MPa and 0.245 MPa. These outcomes further affirm the robustness, adaptability, and generalizability of the method for predicting leaf water potential based on RGB imagery combined with multi-source data across different geographical settings. [ABSTRACT FROM AUTHOR]

Accurate ultra-short-term wind speed prediction is critical for various engineering applications. Although decomposition-integration methods are widely used for this purpose, several challenges remain. This study addresses these challenges by integrating two-stage feature extraction, a combination weight prediction model, and intelligent integration to improve prediction accuracy. In the decomposition phase, two advanced methods are employed to reduce data complexity and extract comprehensive features. During reconstruction, multiscale sample entropy is applied to balance computational efficiency with model complexity. To overcome the limitations of individual forecasting models, a combined prediction model incorporating two deep learning approaches is developed, with weights adaptively optimized using the Sparrow Search Algorithm. Additionally, to address the variability in subsequence contributions, intelligent weight integration based on deep learning models is implemented, ensuring robust and accurate final predictions. Validation using wind speed data from three Hong Kong Observatory weather stations demonstrates that the proposed method outperforms benchmark models, achieving more-satisfactory accuracy, stability, and robustness. [ABSTRACT FROM AUTHOR]

This study explores the impact of stochastic resetting on the random walk dynamics within scale-free (u , v) -flowers. Utilizing the generating function technique, we develop a recursive relationship for the generating function of the first passage time and establish a connection between the mean first passage time with and without resetting. Our investigation spans multiple scenarios, with the random walker starting from various positions and aiming to reach different target nodes, allowing us to identify the optimal resetting probability that minimizes the mean first passage time for each case. We demonstrate that stochastic resetting significantly improves search efficiency, especially in larger networks. These findings underscore the effectiveness of stochastic resetting as a strategy for optimizing search algorithms in complex networks, offering valuable applications in domains such as biological transport, data networks, and search processes where rapid and efficient exploration is vital. [ABSTRACT FROM AUTHOR]

Background: Accurately identifying the socio-demographic information of customers is crucial for utilities. It enables them to efficiently deliver personalized energy services and manage distribution networks. In recent years, machine learning-based data-driven methods have gained popularity compared to traditional survey-based approaches, owing to their time and cost efficiency, as well as the availability of a large amount of high-frequency smart meter data. Methods: In this paper, we propose a new method that harnesses the power of neural architecture search to automatically design deep neural network architectures tailored for identifying various socio-demographic information of customers using smart meter data. We designed a search space based on a novel channel attention fully convolutional network architecture. Furthermore, we developed a search algorithm based on Bayesian optimization to effectively explore the space and identify high-performing architectures. Results: The performance of the proposed method was evaluated and compared with a set of machine learning and deep learning baseline methods using a smart meter dataset widely used in this research area. Our results show that the deep neural network architectures designed automatically by our proposed method significantly outperform all baseline methods in addressing the socio-demographic questions investigated in our study. [ABSTRACT FROM AUTHOR]

Species distribution modeling is fundamental to biodiversity, evolution, conservation science, and the study of invasive species. Given environmental data and species distribution data, model selection techniques are frequently used to help identify relevant features. Existing studies aim to find the relevant features by selecting the best models using different criteria, and they deem the predictors in the best models as the relevant features. However, they mostly consider only a given model family, making them vulnerable to model family misspecification. To address this issue, this paper introduces the Bayesian information-theoretic minimum message length (MML) principle to species distribution model selection. In particular, we provide a framework that allows the message length of models from multiple model families to be calculated and compared, and by doing so, the model selection is both accurate and robust against model family misspecification and data aggregation. To find the relevant features efficiently, we further develop a novel search algorithm that does not require calculating the message length for all possible subsets of features. Experimental results demonstrate that our proposed method outperforms competing methods by selecting the best models on both artificial and real-world datasets. More specifically, there was one test on artificial data that all methods got wrong. On the other 10 tests on artificial data, the MML method got everything correct, but the alternative methods all failed on a variety of tests. Our real-world data pertained to two plant species from Barro Colorado Island, Panama. Compared to the alternative methods, for both the plant species, the MML method selects the simplest model while also having the overall best predictions. [ABSTRACT FROM AUTHOR]

Bipartite graphs are widely used for modelling various real-world scenarios characterized with binary relations, such as, scholarly articles recommendation with author-paper relations, and product recommendation with user-product relations. Particularly, maximum butterfly as a special cohesive subgraph of bipartite graphs, is playing an critical role in many promising application such as recommendation systems and research groups detection. Enumerating maximal butterfly has been proved to be a NP-hard and suffers time and space complexity. To conquer this challenge, this paper pioneers a novel problem called maximal butterfly generators search (MBGS) for facilitating the detection of maximal butterflies. The MBGS problem is to find a subgraph B of G such that maximize the number of butterflies in B and it is mathematically proved to NP-Hard. To address this problem, an equivalence relation theorem between maximum butterfly generator and maximum butterfly concept is presented. Furthermore, an effective MBGS search algorithm is proposed. Extensive experiments on real-world networks with ground-truth communities and interesting case studies validated the effectiveness and efficiency of our MBGS model and algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a cable defect assessment method based on a mixed-domain multi-feature set derived from overall harmonic signals. Four typical defect types—thermal ageing, cable moisture, excessive bending, and insulation damage—were simulated under laboratory conditions. Grounding current tests and Variational Mode Decomposition (VMD) time series analysis were performed on the test samples to extract the overall harmonic sequences in the grounding current. Mixed-domain multi-feature set is then formed through feature extraction and validity analysis. To optimize the assessment performance, a Support Vector Machine (SVM) classifier optimized by the Sparrow Search Algorithm (SSA) was constructed. The results show that different defects lead to significantly differentiated harmonic distortions in the grounding currents, which has proved to be a reliable data basis for cable defect assessment. The proposed method refines the data information and achieves the most accurate recognition of cable defects, which may contribute to the reliable operation of power networks. [ABSTRACT FROM AUTHOR]