Search Results (1,286)

In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging the integration of disassembly and assembly lines into a unified closed-loop production system. In this work, a hybrid production line that combines disassembly and assembly processes, incorporating human–machine collaboration, is designed based on the traditional disassembly line. A mathematical model is proposed to address the human–machine collaboration disassembly and assembly hybrid line balancing problem in this layout. To solve the model, an evolutionary learning-based whale optimization algorithm is developed. The experimental results show that the proposed algorithm is significantly faster than CPLEX, particularly for large-scale disassembly instances. Moreover, it outperforms CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while maintaining high solution quality. Full article

Finite control set model predictive control (FCS-MPC) is an attractive control method for electric drives. This is primarily due to the ease of implementation and robust responses. When applied to rotor current control of the Doubly Fed Induction Generator (DFIG), FCS-MPC has thus far exhibited promising results when compared to the conventional Proportional Integral control strategy. Recently, there has been research conducted regarding the reduction in switching frequency of FCS-MPC. Preliminary studies indicate that a reduction in switching frequency will result in larger current ripples and a greater total harmonic distortion (THD). However, research in this area is limited. The aim of this study is two-fold. Firstly, an indication into the effect of weighting factor magnitude on current ripple is provided. Thereafter, the research work provides insight into the effect of such weighting factor on the overall current ripple of FCS-MPC applied to the DFIG and attempts to determine an optimal weighting factor which will simultaneously reduce the switching frequency and keep the current ripple within acceptable limits. To tune the relevant weighting factor, the utilization of swam intelligence is deployed. Three swarm intelligence techniques, particle swarm optimization, the African Vulture Optimization Algorithm, and the Gorilla Troops Optimizer (GTO), are applied to achieve the optimal weighting factor. When applied to a 2 MW DFIG, the results indicated that owing to their strong exploitation capability, these algorithms were able to successfully reduce the switching frequency. The GTO exhibited the overall best results, boasting steady-state errors of 0.03% and 0.02% for the rotor direct and quadrature currents whilst reducing the switching frequency by up to 0.7%. However, as expected, there was a minor increase in the current ripple. A robustness test indicated that the use of metaheuristics still produces superior results in the face of changing operating conditions. The results instill confidence in FCS-MPC as the control strategy of choice, as wind energy conversion systems continue to penetrate the energy sector. Full article

This paper investigates secrecy solutions for integrated sensing and communication (ISAC) systems, leveraging the combination of a reflecting intelligent surface (RIS) and an unmanned aerial vehicle (UAV) to introduce new degrees of freedom for enhanced system performance. Specifically, we propose a secure ISAC system supported by a UAV-mounted RIS, where an ISAC base station (BS) facilitates secure multi-user communication while simultaneously detecting potentially malicious radar targets. Our goal is to improve parameter estimation performance, measured by the Cramér–Rao bound (CRB), by jointly optimizing the UAV position, transmit beamforming, and RIS beamforming, subject to constraints including the UAV flight area, communication users’ quality of service (QoS) requirements, secure transmission demands, power budget, and RIS reflecting coefficient limits. To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. Our numerical results validate the effectiveness of the proposed scheme and demonstrate the potential of employing UAV-mounted RIS in ISAC systems to enhance radar sensing capabilities. Full article

A protein complex can be regarded as a functional module developed by interacting proteins. The protein complex has attracted significant attention in bioinformatics as a critical substance in life activities. Identifying protein complexes in protein–protein interaction (PPI) networks is vital in life sciences and biological activities. Therefore, significant efforts have been made recently in biological experimental methods and computing methods to detect protein complexes accurately. This study proposed a new method for PPI networks to facilitate the processing and development of the following algorithms. Then, a combination of the improved density peaks clustering algorithm (DPC) and the fuzzy C-means clustering algorithm (FCM) was proposed to overcome the shortcomings of the traditional FCM algorithm. In other words, the rationality of results obtained using the FCM algorithm is closely related to the selection of cluster centers. The objective function of the FCM algorithm was redesigned based on ‘high cohesion’ and ‘low coupling’. An adaptive parameter-adjusting algorithm was designed to optimize the parameters of the proposed detection algorithm. This algorithm is denoted as the DFPO algorithm (DPC-FCM Parameter Optimization). Finally, the performance of the DFPO algorithm was evaluated using multiple metrics and compared with over ten state-of-the-art protein complex detection algorithms. Experimental results indicate that the proposed DFPO algorithm exhibits improved detection accuracy compared with other algorithms. Full article

A cascade control structure (CCS) is still the most commonly used control scheme in variable speed control (VSC) electrical drives with alternating current (AC) motors. Several tuning methods are used to select the coefficients of controllers applied in CCS. These approaches can be divided into analytical, empirical, and heuristic ones. Regardless of the tuning method used, there is still a question of whether the CCS is tuned optimally in terms of considered performance indicators to provide high-performance behavior of the electrical drive. Recently, artificial intelligence-based methods, e.g., swarm-based metaheuristic algorithms (SBMAs), have been extensively examined in this field, giving promising results. Moreover, the intensive development of artificial intelligence (AI) assistants based on large language models (LLMs) supporting decision-making processes is observed. Therefore, it is worth examining the ability of LLMs to tune the CCS in the VSC electrical drive. This paper investigates tuning methods for the cascade control structure equipped with PI-type current and angular velocity controllers for PMSM drive. Sets of CCS parameters from electrical engineers with different experiences are compared with reference solutions obtained by using the SBMA approach and LLMs. The novel LLM-based Tuning Assistant (TA) is developed and trained to improve the quality of responses. Obtained results are assessed regarding the drive performance, number of attempts, and time required to accomplish the considered task. A quantitative analysis of LLM-based solutions is also presented. The results indicate that AI-based tuning methods and the properly trained Tuning Assistant can significantly improve the performance of VSC electrical drives, while state-of-the-art LLMs do not guarantee high-performance drive operation. Full article

Feature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. Afterwards, each approach manipulates the solutions obtained by both algorithms in a different way. The objective of this combination is to overcome the GWO stuck-in-local-optima problem that might occur by leveraging the PSO-wide search space exploration ability on the solutions obtained by GWO. Both S-shaped and V-shaped binary transfer functions were used to convert the continuous solutions obtained from each proposed approach to their corresponding binary versions. The three proposed approaches were evaluated using nine small-instance, high-dimensional, cancer-related human gene expression datasets. A set of comparisons were made against the original binary versions of both GWO and PSO algorithms and against eight state-of-the-art feature selection binary optimizers in addition to one of the recent binary optimizers that combines PSO with GWO. The evaluation results showed that one of the proposed S-shaped and V-shaped approaches achieved 0.9 and 0.95 average classification accuracy, respectively, while selecting the fewest number of features. The results also confirmed the superiority of one of the proposed V-shaped approaches when compared with the original binary GWO and PSO approaches. Moreover, the results confirmed the superiority, in most of the datasets, of one of the three approaches over the state-of-the-art approaches. Finally, the results revealed that the best approach in terms of classification accuracy, fitness value, and number of selected features had the highest computational complexity. Full article

The Dung Beetle Optimization Algorithm (DBO) is characterized by its great convergence accuracy and quick convergence speed. However, like other swarm intelligent optimization algorithms, it also has the disadvantages of having an unbalanced ability to explore the world and to use local resources, as well as being prone to settling into local optimal search in the latter stages of optimization. In order to address these issues, this research suggests a multi-strategy fusion dung beetle optimization method (MSFDBO). To enhance the quality of the first solution, the refractive reverse learning technique expands the algorithm search space in the first stage. The algorithm’s accuracy is increased by adding an adaptive curve to control the dung beetle population size and prevent it from reaching a local optimum. In order to improve and balance local exploitation and global exploration, respectively, a triangle wandering strategy and a fusion subtractive averaging optimizer were later added to Rolling Dung Beetle and Breeding Dung Beetle. Individual beetles will congregate at the current optimal position, which is near the optimal value, during the last optimization stage of the MSFDBO; however, the current optimal value could not be the global optimal value. Thus, to variationally perturb the global optimal solution (so that it leaps out of the local optimal solution in the final optimization stage of the MSFDBO) and to enhance algorithmic performance (generally and specifically, in the effect of optimizing the search), an adaptive Gaussian–Cauchy hybrid variational perturbation factor is introduced. Using the CEC2017 benchmark function, the MSFDBO’s performance is verified by comparing it to seven different intelligence optimization algorithms. The MSFDBO ranks first in terms of average performance. The MSFDBO can lower the labor and production expenses associated with welding beam and reducer design after testing two engineering application challenges. When it comes to lowering manufacturing costs and overall weight, the MSFDBO outperforms other swarm intelligence optimization methods. Full article

This paper presents an efficiency optimization method for laser wireless power transmission (LWPT) system, focusing on the coordination and control of multiple laser diodes. A distributed laser wireless power transmission (D-LWPT) system is proposed, which includes multiple independent and parallel power transmission chains. The system has the characteristics of power scalability, redundancy, and control flexibility. The efficiency characteristics of each key component in the LWPT system are discussed. Due to the internal losses of the laser, the transmission efficiency is also affected by the transmission power. For distributed architecture, its flexibility allows for the rational allocation of transmission power. To achieve optimal efficiency, a central scheduling controller is designed to regulate the current of LDs. A swarm intelligence-based optimization algorithm is used to determine the optimal operating current. This significantly improves the system’s efficiency and ensures real-time control. Experimental results validate the effectiveness of the proposed techniques. The DC to DC efficiency of the power transmission chain can reach over 14%, and the photovoltaic array can output a maximum power of over 130 W. The impact of beam combination on the efficiency and output power of PV arrays is less than 3%, indicating that the distributed structure does not affect system performance. The experimental results show that the proposed efficiency optimization method has excellent power following performance (algorithm execution time < 10 ms) and effective efficiency optimization performance. Under light load conditions, the LDs’ efficiency is optimized from 27.5% to 45.0%, and under medium load conditions, it is optimized from 41.5% to 44.5%. This distributed structure and efficiency optimization method provide a solution for improving the performance of LWPT systems. Full article

In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address the problem of image classification when data are limited. Traditional models require a large amount of labeled data for training, while few-shot learning trains models using only a small number of samples (just a few samples per class) to recognize new categories. EMNet shows its potential for bio-inspired algorithms in optimizing feature extraction and enhancing generalization capabilities. It features two key modules: Enhanced Self-Correlated Attention (ESCA) and Multi-Branch Joint Module (MBJ Module). EMNet tackles two main challenges in few-shot learning: how to make an effective important feature extraction and enhancement in images, and improving generalization to new categories. The ESCA module boosts the precision in extracting crucial local features, enhancing classification accuracy. The MBJ module focuses on shared features across images, emphasizing similarities within classes and subtle differences between them. This enhances model adaptability and generalization to new categories. Experimental results show that our model performs better than existing models in one-shot and five-shot tasks on mini-ImageNet, CUB-200, and CIFAR-FS datasets, which proves the proposed model to be an efficient end-to-end solution for few-shot image classification. In the five-way one-shot and five-way five-shot experiments on the CUB-200-2011 dataset, EMNet achieved classification accuracies that were 1.27 and 0.54 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the miniImageNet dataset, EMNet’s classification accuracies were 0.02 and 0.48 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the CIFAR-FS dataset, EMNet’s classification accuracies were 0.19 and 0.18 percentage points higher than those of RENet. Full article

This paper discusses the modeling and solution of a cold chain logistics (CCL) problem using artificial intelligence of things (AIoT). The presented model aims to reduce the costs of the entire CCL network by maintaining the minimum quality of cold products distributed to customers. This study considers equipping distribution centers and trucks with IoT tools and examines the advantages of using these tools to reduce logistics costs. Also, four algorithms based on artificial intelligence (AI), including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Emperor Penguin Optimizer (EPO), have been used in solving the mathematical model. The analysis results show that equipping trucks and distribution centers with the Internet of Things has increased the total costs by 15% compared to before. This approach resulted in a 26% reduction in operating costs and a 60% reduction in transportation costs. As a result of using the Internet of Things, total costs have been reduced by 2.78%. Furthermore, the performance of AI algorithms showed that the high speed of these algorithms is guaranteed against the high accuracy of the obtained results. So, EPO has achieved the optimal value of the objective function compared to a 70% reduction in the solution time. Further analyses show the effectiveness of EPO in the indicators of average objective function, average RPD error, and solution time. The results of this paper help managers understand the need to create IoT infrastructure in the distribution of cold products to customers. Because implementing IoT devices can offset a large portion of transportation and energy costs, this paper provides management solutions and insights at the end. As a result, there is a need to deploy IoT tools in other parts of the mathematical model and its application. Full article

To improve the operational efficiency of electric vehicle (EV) charging infrastructure, this paper proposes a multi-stage hybrid planning method for charging stations (CSs) based on graph auto-encoder (GAE). First, the network topology and dynamic interaction process of the coupled “Vehicle-Station-Network” system are characterized as a graph-structured model. Second, in the first stage, a GAE-based deep neural network is used to learn the graph-structured model and identify and classify different charging station (CS) types for the network nodes of the coupled system topology. The candidate CS set is screened out, including fast-charging stations (FCSs), fast-medium-charging stations, medium-charging stations, and slow-charging stations. Then, in the second stage, the candidate CS set is re-optimized using a traditional swarm intelligence algorithm, considering the interests of multiple parties in CS construction. The optimal CS locations and charging pile configurations are determined. Finally, case studies are conducted within a practical traffic zone in Hong Kong, China. The existing CS planning methods rely on simulation topology, which makes it difficult to realize efficient collaboration of charging networks. However, the proposed scheme is based on the realistic geographical space and large-scale traffic topology. The scheme determines the station and pile configuration through multi-stage planning. With the help of an artificial intelligence (AI) algorithm, the user behavior characteristics are captured adaptively, and the distribution rule of established CSs is extracted to provide support for the planning of new CSs. The research results will help the power and transportation departments to reasonably plan charging facilities and promote the coordinated development of EV industry, energy, and transportation systems. Full article

Multi-agent reinforcement learning (MARL) is characterized by its simple structure and strong adaptability, which has led to its widespread application in the field of path planning. To address the challenge of optimal path planning for mobile agent clusters in uncertain environments, a multi-objective dynamic path planning model (MODPP) based on multi-agent deep reinforcement learning (MADRL) has been proposed. This model is suitable for complex, unstable task environments characterized by dimensionality explosion and offers scalability. The approach consists of two components: an action evaluation module and an action decision module, utilizing a centralized training with decentralized execution (CTDE) training architecture. During the training process, agents within the cluster learn cooperative strategies while being able to communicate with one another. Consequently, they can navigate through task environments without communication, achieving collision-free paths that optimize multiple sub-objectives globally, minimizing time, distance, and overall costs associated with turning. Furthermore, in real-task execution, agents acting as mobile entities can perform real-time obstacle avoidance. Finally, based on the OpenAI Gym platform, environments such as simple multi-objective environment and complex multi-objective environment were designed to analyze the rationality and effectiveness of the multi-objective dynamic path planning through minimum cost and collision risk assessments. Additionally, the impact of reward function configuration on agent strategies was discussed. Full article

WSNs are significant components of modern IoT systems, which typically operate in resource-constrained environments integrated with fog and cloud computing to achieve scalability and real-time performance. Integrating these systems brings challenges such as security threats, scalability bottlenecks, and energy constraints. In this work, we propose a bioinspired blockchain framework aimed at addressing those challenges through the emulation of biological immune adaptation mechanisms, such as the self-recovery of swarm intelligence. It integrates lightweight blockchain technology with bioinspired algorithms, including an AIS for anomaly detection and a Proof of Adaptive Immunity Consensus mechanism for secure resource-efficient blockchain validation. Experimental evaluations give proof of the superior performance reached within this framework: up to 95.2% of anomaly detection accuracy, average energy efficiency of 91.2% when the traffic flow is normal, and latency as low as 15.2 ms during typical IoT scenarios. Moreover, the framework has very good scalability since it can handle up to 500 nodes with only a latency of about 6.0 ms. Full article

With the increasing number of satellites and rising user demands, the volume of satellite data transmissions is growing significantly. Existing scheduling systems suffer from unequal resource allocation and low transmission efficiency. Therefore, effectively addressing the large-scale multi-objective satellite data transmission scheduling problem (SDTSP) within a limited timeframe is crucial. Typically, swarm intelligence algorithms are used to address the SDTSP. While these methods perform well in simple task scenarios, they tend to become stuck in local optima when dealing with complex situations, failing to meet mission requirements. In this context, we propose an improved method based on the minimum angle particle swarm optimization (MAPSO) algorithm. The MAPSO algorithm is encoded as a discrete optimizer to solve discrete scheduling problems. The calculation equation of the sine function is improved according to the problem’s characteristics to deal with complex multi-objective problems. This algorithm employs a minimum angle strategy to select local and global optimal particles, enhancing solution efficiency and avoiding local optima. Additionally, the objective space and solution space exhibit symmetry, where the search within the solution space continuously improves the distribution of fitness values in the objective space. The evaluation of the objective space can guide the search within the solution space. This method can solve multi-objective SDTSPs, meeting the demands of complex scenarios, which our method significantly improves compared to the seven algorithms. Experimental results demonstrate that this algorithm effectively improves the allocation efficiency of satellite and ground station resources and shortens the transmission time of satellite data transmission tasks. Full article

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applications, lack the aspect of empathy, known to improve cooperation in human teams. Emphasizing both cognitive and emotional aspects of empathy, the introduced model navigates communication uncertainties and ambiguities, transforming these challenges into opportunities for learning and adaptation in dynamic environments. A significant feature of this model is its handling of imprecision through fuzzy logic, using fuzzy similarity measures in the decision process. The main objective of the presented research is to introduce a new model for improving cooperativeness in multi-agent systems with the use of cognitive empathy. Future research focus on implementing the model on physical platform and optimize the artificial empathy algorithms in the decision-making module. Full article