Search Results (2,256)

We consider a complex control problem: making a monopod accurately reach a target with a single jump. The monopod can jump in any direction at different elevations of the terrain. This is a paradigm for a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimization-based techniques. Reinforcement learning (RL) is an interesting alternative, but an end-to-end approach in which the controller must learn everything from scratch can be non-trivial with a sparse-reward task like jumping. Our solution is to guide the learning process within an RL framework leveraging nature-inspired heuristic knowledge. This expedient brings widespread benefits, such as a drastic reduction of learning time, and the ability to learn and compensate for possible errors in the low-level execution of the motion. Our simulation results reveal a clear advantage of our solution against both optimization-based and end-to-end RL approaches. Full article

Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput and lower power consumption. Firstly, maximizing the throughput and minimizing power consumption are formulated as a constrained optimization problem. Then, the obtained solution, alongside a heuristic solution, is compared against the solutions to existing approaches, showing our proposed strategy is drastically superior in terms of both power efficiency and system throughput. This study is then concluded by employing artificial intelligence techniques in multi-objective optimization, namely random forest regression, particle swarm, and genetic algorithms, to balance out the trade-offs between maximizing the throughput and power efficiency and minimizing energy consumption. These investigations demonstrate the potential of employing the proposed LTE-LAA architecture in addressing the requirements of next-generation HetNets in terms of power, throughput, and green scalability. Full article

This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson’s Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques, Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Models—GMMs, Expectation Maximization—EM, Logistic Regression—LoR, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF, are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data. Full article

Electromagnetic actuators, characterized by their lack of pneumatic or hydraulic circuits, rapid response, and ease of control, have the potential to significantly enhance the dynamic performance of automotive active suspensions. However, the complexity associated with their models and the calibration of control parameters hampers the efficiency of control design. To address this issue, this paper proposes a reinforcement learning vibration control strategy for electromagnetic active suspension. Firstly, a half-car dynamic model with electromagnetic active suspension is established. Considering the unknown dynamics of the actuator and its preset convergence performance, an optimal control method based on reinforcement learning is investigated. Secondly, a heuristic PI adaptive dynamic programming algorithm is presented. This method can update to the optimal control solution without requiring model parameters or initial design parameters. Finally, the energy consumption and dynamic performance of this method are analyzed through rapid prototyping control simulation. The results show that the ride comfort of the vehicle suspension can be improved with the given preset convergence rate. Full article

This paper proposes a new meta-heuristic optimization algorithm, the crown growth optimizer (CGO), inspired by the tree crown growth process. CGO innovatively combines global search and local optimization strategies by simulating the growing, sprouting, and pruning mechanisms in tree crown growth. The pruning mechanism balances the exploration and exploitation of the two stages of growing and sprouting, inspired by Ludvig’s law and the Fibonacci series. We performed a comprehensive performance evaluation of CGO on the standard testbed CEC2017 and the real-world problem set CEC2020-RW and compared it to a variety of mainstream algorithms such as SMA, SKA, DBO, GWO, MVO, HHO, WOA, EWOA, and AVOA. The best result of CGO after Friedman testing was 1.6333/10, and the significance level of all comparison results under Wilcoxon testing was lower than 0.05. The experimental results show that the mean and standard deviation of repeated CGO experiments are better than those of the comparison algorithm. In addition, CGO also achieved excellent results in specific applications of robot path planning and photovoltaic parameter extraction, further verifying its effectiveness and broad application potential in practical engineering problems. Full article

The capacitated multi-item lot-sizing problem, referred to as the CLSP, is to determine the lot sizes of products in each period in a given planning horizon of finite periods, meeting the product demands and resource limits in each period, and to minimize the total cost, consisting of the production, inventory holding, and setup costs. CLSPs are often encountered in industry production settings and they are considered NP-hard. In this paper, we propose a Lagrange relaxation (LR) approach for their solution. This approach relaxes the capacity constraints to the objective function and thus decomposes the CLSP into several uncapacitated single-item problems, each of which can be easily solved by dynamic programming. Feasible solutions are achieved by solving the resulting transportation problems and a fixup heuristic. The Lagrange multipliers in the relaxed problem are updated by using subgradient optimization. The experimental results show that the LR approach explores high-quality solutions and has better applicability compared with other commonly used solution approaches in the literature. Full article

Data security is one of the biggest concerns in the modern world due to advancements in technology, and cryptography ensures that the privacy, integrity, and authenticity of such information are safeguarded in today’s digitally connected world. In this article, we introduce a new technique for the construction of non-linear components in block ciphers. The proposed S-box generation process is a transformational procedure through which the elements of a finite field are mapped onto highly nonlinear permutations. This transformation is achieved through a series of algebraic and combinatorial operations. It involves group actions on some pairs of two Galois fields to create an initial S-box $P_{r}Sbox$, which induces a rich algebraic structure. The post S-box $P_{o}Sbox$, which is derived from heuristic group-based optimization, leads to high nonlinearity and other important cryptographic parameters. The proposed S-box demonstrates resilience against various attacks, making the system resistant to statistical vulnerabilities. The investigation reveals remarkable attributes, including a nonlinearity score of 112, an average Strict Avalanche Criterion score of 0.504, and LAP (Linear Approximation Probability) score of 0.062, surpassing well-established S-boxes that exhibit desired cryptographic properties. This novel methodology suggests an encouraging approach for enhancing the security framework of block ciphers. In addition, we also proposed a three-step image encryption technique comprising of Row Permutation, Bitwise XOR, and block-wise substitution using $P_{o}Sbox$. These operations contribute to adding more levels of randomness, which improves the dispersion across the cipher image and makes it equally intense. Therefore, we were able to establish that the approach works to mitigate against statistical and cryptanalytic attacks. The PSNR, UACI, MSE, NCC, AD, SC, MD, and NAE data comparisons with existing methods are also provided to prove the efficiency of the encryption algorithm. Full article

Inland waterway transport is considered a critical component of sustainable maritime transportation and is subject to strict legal regulations on fuel quality. However, crew members often prefer cheaper, inferior fuels for economic reasons, making government inspections crucial. To address this issue, we formulate the ship selection and inspection scheduling problem into an integer programming model under a multi-inspector and multi-location scenario, alongside a more compact symmetry-eliminated model. The two models are developed based on ship itinerary information and inspection resources, aiming to maximize the total weight of the inspected ships. Driven by the unique property of the problem, a customized heuristic algorithm is also designed to solve the problem. Numerical experiments are conducted using the ships sailing on the Yangtze River as a case study. The results show that, from the perspective of the computation time, the compact model is 102.07 times faster than the original model. Compared with the optimal objectives value, the gap of the solution provided by our heuristic algorithm is 0.37% on average. Meanwhile, our algorithm is 877.19 times faster than the original model, demonstrating the outstanding performance of the proposed algorithm in solving efficiency. Full article

The k shortest paths problem finds applications in multiple fields. Of particular interest in the transportation field is the variant of finding k simple shortest paths (KSSP), which has a higher complexity. This research presents a novel label-setting algorithm for the multi-destination KSSP problem in directed networks that obviates repeated applications of the algorithm to each destination (necessary in existing deviation-based algorithms), resulting in a significant computational speedup. It is shown that the proposed algorithm is exact and flexible enough to handle several variants of the problem by appropriately modifying the termination condition. Theoretically, it is also shown to be faster than state-of-the-art algorithms in sparse and dense networks whenever the number of labels created is sub-polynomial in network size. A heuristic method and optimized data structures are proposed to improve the algorithm’s scalability and worst-case performance. The computational results show that the proposed heuristic provides two to three orders of magnitude computational time speedups (29–1416 times across different networks) with negligible loss in solution quality (maximum average deviation of 0.167% from the optimal solution). Finally, a practical application of the proposed method is illustrated to determine the gravity of an edge (relative structural importance) in a network. Full article

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account. Full article

This research develops a method for synthesizing linguistic models of fuzzy systems with fuzzy input and output parameters that are described by linguistic variables. Based on the proposed method, linguistic models of the Title 1000 catalytic cracking unit for heavy residues at the Shymkent oil refinery are developed, describing the dependence of the volume and quality of gasoline on the input and operating parameters of the facility, which are fuzzy. It is substantiated that the use of a fuzzy approach, which allows the use of the experience, knowledge, and intuition (intelligence) of the decision maker and subject matter experts, is the most suitable effective method for synthesizing models of complex, fuzzily described objects and processes for comparison with other methods. The main idea of the proposed work is to solve the problems of shortage and fuzziness of initial information when developing models and optimizing the operating modes of a catalytic cracking unit through the use of knowledge, experience, and intuition of experts in this field. To solve the problems of the shortage of initial quantitative information and the fuzziness of available information when developing mathematical models, it is proposed to systematically use statistical methods, expert assessment methods, and a heuristic method based on fuzzy logic. The scientific novelty of the research lies in the development of a method for synthesizing linguistic models in a fuzzy environment and an algorithm for its implementation, which makes it possible to describe the dependence of the fuzzy values of the object’s output parameters on its fuzzy input and operating parameters. The proposed approach allows the formalization and synthesis of models of fuzzily described objects when other methods of model development are not applicable or do not give the expected results. The results of the work were simulated in the MATLAB Fuzzy Logic Toolbox. Full article

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators—such as shelf access frequency and the total processing time of the RMFS—are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency. Full article

This study addresses the critical challenge of data imbalance in credit card fraud detection (CCFD), a significant impediment to accurate and reliable fraud prediction models. Fraud detection (FD) is a complex problem due to the constantly evolving tactics of fraudsters and the rarity of fraudulent transactions compared to legitimate ones. Efficiently detecting fraud is crucial to minimize financial losses and ensure secure transactions. By developing a framework that transitions from imbalanced to balanced data, the research enhances the performance and reliability of FD mechanisms. The strategic application of Meta-heuristic optimization (MHO) techniques was accomplished by analyzing a dataset from Kaggle’s CCF benchmark datasets, which included data from European credit-cardholders. They evaluated their capability to pinpoint the smallest, most relevant set of features, analyzing their impact on prediction accuracy, fitness values, number of selected features, and computational time. The study evaluates the effectiveness of 15 MHO techniques, utilizing 9 transfer functions (TFs) that identify the most relevant subset of features for fraud prediction. Two machine learning (ML) classifiers, random forest (RF) and support vector machine (SVM), are used to evaluate the impact of the chosen features on predictive accuracy. The result indicated a substantial improvement in model efficiency, achieving a classification accuracy of up to 97% and reducing the feature size by up to 90%. In addition, it underscored the critical role of feature selection in optimizing fraud detection systems (FDSs) and adapting to the challenges posed by data imbalance. Additionally, this research highlights how machine learning continues to evolve, revolutionizing FDSs with innovative solutions that deliver significantly enhanced capabilities. Full article

In the context of the carbon trading mechanism, this study investigated a multi-visit vehicle routing problem with a truck-drone collaborative delivery model. This issue involves the route of a truck fleet and drones, each truck equipped with a drone, allowing drones to provide services to multiple customers. Considering the carbon emissions during both the truck’s travel and the drone’s flight, this study established a mixed integer programming model to minimize the sum of fixed costs, transportation costs, and carbon trading costs. A two-stage heuristic algorithm was proposed to solve the problem. The first stage employed a “Scanning and Heuristic Insertion” algorithm to generate an initial feasible solution. In the second stage, an enhanced variable neighborhood search algorithm was designed with problem-specific neighborhood structures and customized search strategies. The effectiveness of the proposed algorithm was validated with numerical experiments. Additionally, this study analyzed the impact of various factors on carbon trading costs, revealing that there exists an optimal combination of drones and trucks. It was also observed that changes in carbon quotas do not affect carbon emissions but do alter the total delivery costs. These results provide insights for logistics enterprise operations management and government policy-making. Full article

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. Full article