Sustainable scheduling is getting more and more attention with economic globalization and sustainable manufacturing. However, fewer studies on the batch scheduling problem consider energy consumption. This paper condu...
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Sustainable scheduling is getting more and more attention with economic globalization and sustainable manufacturing. However, fewer studies on the batch scheduling problem consider energy consumption. This paper conducts an investigation into the multi-objective hybrid flow shop batch-scheduling problem with the objectives of minimizing both the makespan and electrical energy consumption. The study aims to select the optimal scheduling solution for the problem by considering batch splitting for all products. In this paper, we propose an improvedblackwidowoptimization (IBWO) algorithm to study the problem, which incorporates procreation, cannibalism, and mutation behaviors to maintain the population's diversity and stability. To achieve our objectives, we use the dynamic entropy weight topsis method to select individual spiders. Finally, we use the nature theorem construction method, which relies on the property theorem, to solve the Pareto solution set and derive the optimization scheme for the hybrid flow shop batch scheduling problem. We verify the effectiveness of the proposed IBWO on instances of varying sizes. When we keep all other factors and cases constant, we compare the IBWO to the NSGA2 algorithm and find that it converges faster for both goals and has lower goals than the NSGA2.
In this research work, a novel Content-based image retrieval (CBIR) model with the utilization of deep learning techniques and adaptive concepts is executed. Initially, the required images are collected from benchmark...
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In this research work, a novel Content-based image retrieval (CBIR) model with the utilization of deep learning techniques and adaptive concepts is executed. Initially, the required images are collected from benchmark data sources. The gathered images are fed to the feature extraction process whereas the Adaptive Residual Attention Network (A-RAN) is analyzed to obtain the necessary deep features. Here, the parameters in the RAN are tuned by the improved black widow optimization algorithm (IBWOA). The extracted features are considered for analyzing the multi-similarity measures. Using the similarity score, the images are retrieved from the database for the provided query. By comparing conventional techniques, the precision of the developed model attains 22.05%, 27.69%, 38.7%, and 20.2% better performance than CNN, CapsNet, DBN, and BMWDLNN, respectively. It helps the recommended framework to provide better performance by providing a visual representation of effectively retrieving the relevant content.
Rapid growth of multimedia, storage systems and digital computers has resulted in repositories of multimedia content and large image in recent years. The hospitals with facilities of diagnostic and investigative imagi...
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Rapid growth of multimedia, storage systems and digital computers has resulted in repositories of multimedia content and large image in recent years. The hospitals with facilities of diagnostic and investigative imaging has been generating huge amount of imaging data that are creating tremendous growth while producing collections of medical image. As a result, the creation of an excellent medical picture retrieval system is required to assist physicians in exploring such enormous datasets. This research introduces an improved black widow optimization algorithm (IBWO) for estimating optimal Fuzzy C-means centroids in a Content-Based Image Retrieval (CBIR) system, significantly enhancing computational efficiency and achieving superior average precision compared to traditional methods, thus addressing challenges in managing and retrieving large medical image datasets. To forecast FCM centroids, an optimization approach is used, which reduces complexity and computing time greatly. It is evident from the results that incorporation of optimization techniques certainly reduces the computational complexity and produces improved performance over traditional approaches. The proposed approach IBWO involved in identifying optimal centroids unveils better average precision of 93.45% for four test cases that is superior over comparative techniques.
In heating, ventilation, and air conditioning (HVAC) systems for large office buildings, accurate cooling load prediction facilitates the elaboration of energy-efficient and energy-saving operation strategies for the ...
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In heating, ventilation, and air conditioning (HVAC) systems for large office buildings, accurate cooling load prediction facilitates the elaboration of energy-efficient and energy-saving operation strategies for the system. In this paper, a hybrid prediction model based on gray relational analysis-improved black widow optimization algorithm-temporal convolutional neural network (GRA-IBWOA-TCN) is proposed for cold load prediction of large office buildings. First, the factors influencing cold load in large office buildings were analyzed, with GRA used to identify key features and reduce input data dimensionality for the prediction model. Second, three improvement strategies are proposed to enhance optimization performance at different stages of the blackwidowoptimizationalgorithm, aimed at establishing a prediction model for optimizing TCN hyper-parameters through IBWOA. Finally, the algorithmoptimization and prediction model comparison experiments were conducted with the intra-week dataset (T1) and the weekend dataset (T2) of a large office building as the study samples, respectively. The results show that the mean absolute percentage error values of the GRA-IBWOA-TCN model for the prediction results of the T1 and T2 datasets are 0.581% and 0.348%, respectively, which are 81.1% and 88.3% lower compared to the TCN model, and exhibit the highest prediction accuracy in optimizing the results of the TCN model and the prediction models, such as backpropagation, support vector machine, long short-term memory, and convolutional neural network, with multiple algorithms, good stability, and generalization ability. In summary, the hybrid prediction model proposed in this paper can provide effective technical support for the energy-saving management of HVAC systems in large office buildings.
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