This paper describes an application of NSGA-II as one of Multi-objective Evolutionary Algorithms (MOEAs) to a many-objective Nurse Scheduling in an actual hospitals in Japan and its effectiveness. Although many techni...
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ISBN:
(纸本)9781728159539
This paper describes an application of NSGA-II as one of Multi-objective Evolutionary Algorithms (MOEAs) to a many-objective Nurse Scheduling in an actual hospitals in Japan and its effectiveness. Although many techniques for the actual nurse scheduling have been poposed, they are based on the culture of work styles in Europe or in the US, and then they are not fitted for creating a nurse work schedule in Japan. The nurse scheduling problem has manyobjectives, twelve objectives specially in the problem shown in this paper. Such an optimizationproblem having manyobjectives is generally called a many-objective optimization problem (MaOP), and it is considered that MOEAs such as NSGA-II are not effective. Although MOEA/D and NSGA-III, which are one of MaOEA, are known as effective algorithms for MaOPs, these algorithms, for example, require an so many number of scalarization vectors or appropriate reference set, they are not always easy to apply to real world problems. The MaOEAs are also very sensitive techniques to the vectors or reference set. On the other hand, although it has been pointed out that MOEAs are not suitable for MaOP in verification reports with several benchmarks, there is no fact that MOEAs have been applied to real-world MaOPs and their effectiveness has been denied. Therefore, this paper tries to apply NSGA-II, one of MOEAs, to the practical nurse scheduling problem without omitting or reducing all the objectives, and verify its effectiveness.
Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The ...
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Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The risks reflect the impact of uncertainty and volatility on the resilience of the supply chains. Therefore, there is a need for the enhancement of the production planning in Energy Supply Chains (ESCs), as it enables affordable energy supplies and supports the companies transition to a clean, secure and sustainable energy mix. This study aims to understand the interactive behavior among individuals and optimize their production planning under uncertainty scenarios. In particular, we propose a novel framework to couple an Agent-based Modelling (ABM) and a Co-evolutionary Algorithm (CEA), to realize its capacity to solve a many-objective optimization problem (MaOP) where the profits of multiple agents are concurrently maximized in their interactive transaction processes under normal conditions and uncertain disruption events. For demonstration, we illustrate the proposed approach by considering a five-layer oil and gas ESC model, where uncertainties from multiple sources and the structural dynamics challenge the balance between supply and demand. The results obtained by an integration of a Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) algorithm into ABM show the pricing and orders of the target agents are optimized while the loss of ESC resilience is minimized under uncertainty scenarios, proving its capacity of improving the diversity and the convergence, compared to the classic evolutionary algorithms.
Interval many-objective optimization problems (IMaOPs) involve more than three conflicting objectives with interval parameters. Various real-world applications under uncertainty can be modeled as IMaOPs to solve, so e...
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Interval many-objective optimization problems (IMaOPs) involve more than three conflicting objectives with interval parameters. Various real-world applications under uncertainty can be modeled as IMaOPs to solve, so effectively handling IMaOPs is crucial for solving practical problems. This paper proposes an adaptive interval many-objective evolutionary algorithm with information entropy dominance (IMEA-IED) to tackle IMaOPs. Firstly, an interval dominance method based on information entropy is proposed to adaptively compare intervals. This method constructs convergence entropy and uncertainty entropy related to interval features and innovatively introduces the idea of using global information to regulate the direction of local interval comparison. Corresponding interval confidence levels are designed for different directions. Additionally, a novel niche strategy is designed through interval population partitioning. This strategy introduces a crowding distance increment for improved subpopulation comparison and employs an updated reference vector method to adjust the search regions for empty subpopulations. The IMEA-IED is compared with seven interval optimization algorithms on 60 interval test problems and a practical application. Empirical results affirm the superior performance of our proposed algorithm in tackling IMaOPs.
The framework of decomposing a multi -objectiveoptimizationproblem (MOP) into some MOPs holds considerable promise. However, its advancement is constrained by numerous elements, including the incorrect segmentation ...
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The framework of decomposing a multi -objectiveoptimizationproblem (MOP) into some MOPs holds considerable promise. However, its advancement is constrained by numerous elements, including the incorrect segmentation of the subspaces and the challenges in balancing convergence and diversity. To address these issues, an objective space Decomposition and Clustering-based Evolutionary Algorithm (DCEA) is proposed in this paper. Specifically, DCEA employs K-means clustering to create an appropriate mating pool for each individual without the necessity to predetermine the number of clusters. Within each mating pool, the proposed adaptive evolutionary operator is applied to produce offspring for balancing the convergence and diversity. To enhance the accuracy of partitioning, a refined environmental selection approach utilizing supplementary weight vectors is developed. Additionally, by utilizing historical clustering data, a straightforward approach to periodically adjust reference vectors for the allocation of computational resources is proposed. In experiments, both MOPs and many-objective optimization problems (MaOPs) are used to test DCEA. A total of 27 MOPs and 30 MaOPs are involved and 16 state-of-the-art algorithms are employed to compare with DCEA. Comprehensive experiments show that DCEA is an effective algorithm for solving both MOPs and MaOPs.
Advanced mobile communication and data processing technologies have promoted the development of Internet of Things (IoT), but they have also posed challenges to the distributed federated learning mode in the field of ...
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Advanced mobile communication and data processing technologies have promoted the development of Internet of Things (IoT), but they have also posed challenges to the distributed federated learning mode in the field of Internet of Vehicles (IoV). Faced with a large number of vehicle nodes available for federated training in IoV, the federated learning training task becomes challenging when motivating a large number of vehicles participant. A difficulty posed for federated learning in IoV is heterogeneity challenges caused by massive device participation. Moreover, excessive resource and system maintenance costs associated with a large number of poor-quality devices participating in federated training cannot be ignored. To address these issues, this paper proposes a novel vehicle device selection and aggregation joint optimization model based on a many-objective evolutionary algorithm. The proposed model can be optimized by BiGE algorithm to obtain an optimal subset of vehicle equipment and corresponding weight assignment scheme, thus reducing unnecessary resources waste and budget expenditure while ensuring global model performance. To verify the feasibility of the model, several sets of experiments are conducted to demonstrate that our proposed model has acceptable performance while largely reducing the number budget of participants.
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