| 1. |
- Abba, S.I., et al.
(författare)
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Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling
- 2022
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffeefficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.
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| 2. |
- Abbasi, Shirin, et al.
(författare)
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A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles
- 2023
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Ingår i: Applied Soft Computing. - : Elsevier Ltd. - 1568-4946 .- 1872-9681. ; 143
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Tidskriftsartikel (refereegranskat)abstract
- Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm's objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities; in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm's performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services.
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| 3. |
- Aslani, Mohammad, et al.
(författare)
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A fast instance selection method for support vector machines in building extraction
- 2020
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Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.
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| 4. |
- Deng, Jifei, et al.
(författare)
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Mass customization with reinforcement learning : Automatic reconfiguration of a production line
- 2023
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 145
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the problem of efficient automation system configuration for mass customization in industrial manufacturing. Due to the various demands from customers, production lines need to adjust the process parameters of the machines based on specific quality parameters. Reinforcement learning, which learns from samples, can tackle the problem more efficiently than the currently used methods. Based on the proximal policy optimization and centralized training with decentralized execution, a multi-agent reinforcement learning method (MARL) is proposed to reconfigure process parameters of machines based on the changed specifications. The proposed method has the actor of each agent observing only its own state, the agents are made to collaborate by a centralized critic which observes all the states. To evaluate the method, a steel strip rolling line with six collaborating mills is studied. Simulation results show that the proposed method outperforms the existing methods and state-of-the-art multi-agent reinforcement learning methods in terms of accuracy and computing costs.
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| 5. |
- Feng, Hailin, et al.
(författare)
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Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins
- 2023
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 136
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Tidskriftsartikel (refereegranskat)abstract
- The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.
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| 6. |
- Huotari, Matti, et al.
(författare)
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Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts
- 2021
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 111
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Tidskriftsartikel (refereegranskat)abstract
- A key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory networks and with combined convolutional neural network and long short-term memory networks methods. We used multiple predictors and lagged target signal decomposition results as additional predictors and compared the yielded prediction results with different sets of predictors for each method. For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data. The best model that we derived was validated by a novel walk-forward algorithm that also calculates point-wise confidence intervals for the predictions; we yielded reasonable predictions and confidence intervals for the predictions. Furthermore, we verified this model against five other lithium-ion battery packs; the best model generalised to greater extent to this set of battery packs. The results about the final model suggest that we were able to enhance the results in respect to previously developed models. Moreover, we further validated the model for extracting cycle counts presented in our previous work with data from new forklifts; their battery packs completed around 3000 cycles in a 10-year service period, which corresponds to the cycle life for commercial Nickel–Cobalt–Manganese (NMC) cells.
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| 7. |
- Li, Xiaoming, et al.
(författare)
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Evolutionary computation-based machine learning for Smart City high-dimensional Big Data Analytics
- 2023
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Ingår i: Applied Soft Computing. - : ELSEVIER. - 1568-4946 .- 1872-9681. ; 133
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Tidskriftsartikel (refereegranskat)abstract
- Science and technology development promotes Smart City Construction (SCC) as a most imminent problem. This work aims to improve the comprehensive performance of the Smart City-oriented high-dimensional Big Data Management (BDM) platform and promote the far-reaching development of SCC. It comprehensively optimizes the calculation process of the BDM platform through Machine Learning (ML), reduces the dimension of the data, and improves the calculation effect. To this end, this work first introduces the concept of SCC and the BDM platform application design. Then, it discusses the design concept of using ML technology to optimize the calculation effect of the BDM platform. Finally, the Tensor Train Support Vector Machine (TT-SVM) model is designed based on dimension reduction data processing. The proposed model can comprehensively optimize the BDM platform, and the model is compared with other models and evaluated. The research results show that the accuracy of the reduced dimension classification of the TT-SVM model is more than 95. The lowest average processing time for the model's reduced dimension classification is about 1ms. The model's highest data processing accuracy is about 98%, and the average processing time is between 1.0- 1.5ms. Compared with traditional models and BDM platforms, the proposed model has a breakthrough performance improvement, so it plays an important role in future SCC. This work has achieved a great breakthrough in big data processing, and innovatively improved the application mode of high-dimensional big data technology by integrating multiple technologies. Therefore, the finding provides targeted technical reference for algorithms in BDM platform and contributes to the construction and improvement of Smart City.& COPY; 2022 Elsevier B.V. All rights reserved.
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| 8. |
- Liang, Xinyue, et al.
(författare)
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Decentralized learning of randomization-based neural networks with centralized equivalence
- 2022
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Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 115
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Tidskriftsartikel (refereegranskat)abstract
- We consider a decentralized learning problem where training data samples are distributed over agents (processing nodes) of an underlying communication network topology without any central (master) node. Due to information privacy and security issues in a decentralized setup, nodes are not allowed to share their training data and only parameters of the neural network are allowed to be shared. This article investigates decentralized learning of randomization-based neural networks that provides centralized equivalent performance as if the full training data are available at a single node. We consider five randomization-based neural networks that use convex optimization for learning. Two of the five neural networks are shallow, and the others are deep. The use of convex optimization is the key to apply alternating-direction-method-of-multipliers with decentralized average consensus. This helps us to establish decentralized learning with centralized equivalence. For the underlying communication network topology, we use a doubly-stochastic network policy matrix and synchronous communications. Experiments with nine benchmark datasets show that the five neural networks provide good performance while requiring low computational and communication complexity for decentralized learning. The performance rankings of five neural networks using Friedman rank are also enclosed in the results, which are ELM < RVFL< dRVFL < edRVFL < SSFN.
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| 9. |
- Rabet, Rahmat, et al.
(författare)
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A simheuristic approach towards supply chain scheduling : Integrating production, maintenance and distribution
- 2024
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 153
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Tidskriftsartikel (refereegranskat)abstract
- This study attempts to integrate production, maintenance, and delivery operations among supply chain members. Despite numerous studies in the field of supply chain management, researchers have often overlooked crucial aspects, such as uncertainties in demand and production. For instance, the significant impact of maintenance activities on production flow has been underrepresented in supply chain management literature. This study investigates these gaps in the context of a fertilizer producer case study, which is characterized by seasonal demand and the functional silos syndrome due to old-fashioned management approaches. This study proposes a mathematical model and two multi-objective simheuristics for the Integrated Production, Maintenance, and Distribution Scheduling Problem (IPMDSP) considering demand variation for multiple products and product delivery time-windows using a heterogeneous fleet of vehicles. The IPMDSP is solved using the ϵ-constraint method and simheuristics linking the simulation model to customized and tuned versions of Particle Swarm Optimization (MOPSO) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimization objectives include minimizing maintenance duration, distribution costs, and customer dissatisfaction due to delivery tardiness. The results demonstrate the superiority of the simheuristic empowered by NSGA-II over the MOPSO in solving the IPMDSP. The comparison between the performance of deterministic and stochastic approaches in addressing the problem reveals that neglecting uncertainty caused by maintenance activities can lead to an increase in optimization objectives. Furthermore, the proposed simheuristics achieved significant improvements in minimizing objectives in solving the fertilizer producer case study.
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| 10. |
- Seyed Jalaleddin, Mousavirad, et al.
(författare)
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Automatic clustering using a local search-based human mental search algorithm for image segmentation
- 2020
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Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 96
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Tidskriftsartikel (refereegranskat)abstract
- Clustering is a commonly employed approach to image segmentation. To overcome the problems of conventional algorithms such as getting trapped in local optima, in this paper, we propose an improved automatic clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a recently proposed method to solve complex optimisation problems. In contrast to most existing methods for image clustering, our approach does not require any prior knowledge about the number of clusters but rather determines the optimal number of clusters automatically. In addition, for further improved efficacy, we incorporate local search operators which are designed to make changes to the current cluster configuration.To evaluate the performance of our proposed algorithm, we perform an extensive comparison with several state-of-the-art algorithms on a benchmark set of images and using a variety of metrics including cost function, correctness of the obtained numbers of clusters, stability, as well as supervised and unsupervised segmentation criteria. The obtained results clearly indicate excellent performance compared to existing methods with our approach yielding the best result in 16 of 17 cases based on cost function evaluation, 9 of 11 cases based on number of identified clusters, 13 of 17 cases based on the unsupervised Borsotti image segmentation criterion, and 7 of 11 cases based on the supervised PRI image segmentation metric.
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