A hierarchical population task planning method is presented to enhance the test efficiency and reliability of intelligent technology test ships under various tasks and complex limitations. Firstly, a mathematical mode...
详细信息
A hierarchical population task planning method is presented to enhance the test efficiency and reliability of intelligent technology test ships under various tasks and complex limitations. Firstly, a mathematical model of the vehicle path problem for multi-voyage vessel testing is developed, which aims to minimize the ship's fixed and fuel costs, taking into account the energy and space constraints of an intelligent technology test vessel, as well as practical factors such as the dependencies and temporal relationships between test tasks. Second, to fairly minimize constraint complexity in the planning process, an offshore test task planning architecture based on the concept of hierarchical population is explored and built. This architecture separates task planning into four levels and allocates the tasks to distinct populations. Using this information, a grouping genetic algorithm is suggested based on the characteristics of the population. This algorithm uses a unique coding method to represent task clusters and narrows the range of possible solutions. The issue of the conventional grouping genetic algorithm's vast search space is resolved. Lastly, simulation verification is carried out, and the results show that the method can effectively solve the problem of offshore test task planning for intelligent technology test ships under multi-constraint conditions. It reduces test cost and improves test efficiency.
This paper addresses a variant of the vehicle routing problem with time windows where the goal is to maximize the quality of service delivered to the customer. In the literature, this problem contains three objectives...
详细信息
This paper addresses a variant of the vehicle routing problem with time windows where the goal is to maximize the quality of service delivered to the customer. In the literature, this problem contains three objectives targeted at improving the quality of service. In this paper, we have proposed two evolutionary approaches, viz., a steady-state grouping genetic algorithm and a discrete differential evolution algorithm, to address this problem. The crossover and mutation operators are designed by considering the characteristics of each objective. The proposed approaches are incorporated with various heuristics that provide a set of better initial solutions in comparison to purely random initial solutions. We have also proposed two bounds for each objective. The approaches presented in this paper are tested on the Solomon instances which are considered as the standard benchmark instances for the vehicle routing problem with time windows in the literature. The proposed approaches are compared with the state-of-the-art approach available in the literature. The computational results demonstrate that our approaches are better in terms of solution quality and execution time than the state-of-the-art approach.(c) 2022 Elsevier B.V. All rights reserved.
When a stock portfolio is suggested to inventors, they may need a mechanism to replace stocks when their future prospects are pessimistic. However, existing approaches only consider all assets to find a diverse group ...
详细信息
When a stock portfolio is suggested to inventors, they may need a mechanism to replace stocks when their future prospects are pessimistic. However, existing approaches only consider all assets to find a diverse group stock portfolio (DGSP), which may suffer massive losses as a result. In this paper, an intelligent optimisation algorithm is proposed to obtain a more profitable DGSP with active and inactive stocks. In the coding scheme, not only grouping, stocks, and weighting but also active stock parts are used to represent a DGSP. Two evaluation functions are developed according to five factors, including group balance, modified portfolio satisfaction, price balance, unit balance, and extended diversity factor. These functions are used to assess the fitness of a chromosome. Finally, empirical studies were conducted on two financial datasets to show the merits of the proposed algorithm.
Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelen...
详细信息
Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group geneticalgorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.
Virtual machine(VM) placement is a key technologyto improve data center *** works consider VM placement problem only with respect to physical machine(PM) or network resource ***,efficient VM placement should be implem...
详细信息
Virtual machine(VM) placement is a key technologyto improve data center *** works consider VM placement problem only with respect to physical machine(PM) or network resource ***,efficient VM placement should be implemented by joint optimization of above two *** this paper,a multi-objective VM placement model to minimize the number of active PMs,minimize communication traffic and balance multi-dimensional resource use simultaneously within the data center is *** improved evolutionary multi-objective algorithm: NS-GGA is also designed to tackle this problem,which incorporates the fast nondominated sorting of NSGA-II into the groupinggenetic *** simulation results show that,in most cases,our model and algorithm gains significantly in all aspects and yields better solutions compared to the existing methods.
暂无评论