This study introduces a novel framework for topology optimization in structural design by integrating global and local search algorithms. Specifically, a genetic algorithm (GA) is employed as the global search method,...
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ISBN:
(数字)9798331508272
ISBN:
(纸本)9798331508289
This study introduces a novel framework for topology optimization in structural design by integrating global and local search algorithms. Specifically, a genetic algorithm (GA) is employed as the global search method, leveraging its strengths in handling diverse objective functions while preserving interpretability throughout the optimization process. As a specific example of the framework, a system integrating GA as the global search algorithm and Bidirectional Evolutionary Structural Optimization (BESO) as the local search algorithm is introduced. GA is employed for its global exploration capability, enabling exploration on a diverse set of solutions for a wide range of optimization problems. BESO is applied as a local search method to refine solutions, enhancing the optimization results by precisely adjusting the structural design during the searching process. The effectiveness of this approach is demonstrated through two numerical examples focusing on their own primary objectives: one maximizing the structural stiffness, and the other maximizing the displacement. The results show that the combination of GA and BESO effectively meets the set design goals, highlighting the potential for significant structural design improvements through their synergistic effect, confirming the benefits of combining GA's ability to conduct global exploration with BESO's capacity to fine-tune solutions through local search. This study demonstrates the effectiveness of integrating GA and BESO in structural topology optimization, providing a powerful tool for advancing generative structural design.
High penetrations of the intermittent distributed energy resources in the distribution systems such as rooftop and community solar systems can lead to voltage control and flicker issues. In this study, an efficient va...
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ISBN:
(数字)9798331541125
ISBN:
(纸本)9798331541132
High penetrations of the intermittent distributed energy resources in the distribution systems such as rooftop and community solar systems can lead to voltage control and flicker issues. In this study, an efficient vault-based battery deployment is investigated to mitigate the adverse effects of grid-connected solar systems on voltage rise and flicker with minimum cost. The fast fluctuations in solar power can be remedied by addition of aggregated battery storage systems into the distribution system. A linear programming (LP) optimization problem is used that enables the utility to determine the optimum battery size for both energy and power ratings. Technical constraints such as ramp rate control and charge/discharge cycles factors affecting battery degradation are also taken into consideration. Furthermore, an optimum battery dispatch algorithm is developed through the LP optimization problem. Analysis shows that high power ratings is needed to compensate for rapid fluctuations in the solar profile, whereas the required energy capacity is much less, and using a fast-response storage device along with the battery can significantly reduce the battery size and reduce battery costs.
We consider a probabilistic model for large-scale task allocation problems for multi-agent systems, aiming to determine an optimal deployment strategy that minimizes the overall transport cost. Specifically, we assign...
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Parameter extraction (PE) is a key subproblem of space mapping (SM) design optimization. It consists of a local alignment between the coarse and fine models at each SM iteration. In this work, cognition-driven PE is p...
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ISBN:
(数字)9798331540401
ISBN:
(纸本)9798331540418
Parameter extraction (PE) is a key subproblem of space mapping (SM) design optimization. It consists of a local alignment between the coarse and fine models at each SM iteration. In this work, cognition-driven PE is proposed for SM. In contrast to classical PE, where the full fine model responses are used as targets, the proposed cognitive PE focuses on key features of the fine model response selected from an engineering perspective. It is demonstrated that the proposed cognitive PE approach: 1) yields more accurate extracted parameters regardless of the type of PE objective function employed; and 2) achieves a more meaningful matching to the fine model target response and with less variability. To proof this with independence of the optimization method employed for PE, plots of the PE objective functions are presented over large regions of the coarse model design space. Two synthetic examples are used to support these findings.
This research focuses on the site selection problem of medical waste recovery points, considering its unique characteristics, and constructs an objective function incorporating multiple elements such as recovery point...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
This research focuses on the site selection problem of medical waste recovery points, considering its unique characteristics, and constructs an objective function incorporating multiple elements such as recovery point coverage radius, medical waste capacity constraints, and transportation distances. To efficiently address this complex site selection problem, we employed a genetic algorithm-based site selection model, combined with improved solution strategies to solve the model, significantly enhancing the algorithm's efficiency and quality. In practical implementation, we integrated the powerful mathematical simulation programming tool, Matlab R2021a, with our genetic algorithm model to solve specific case studies. The experimental results validated the effectiveness and practicality of the genetic algorithm model in addressing medical waste recovery point site selection problems, providing a robust theoretical foundation for related decision-makers. This study not only showcases the significant role of computer science in solving complex problems but also offers new insights and methodologies for algorithm optimization and application. It underscores the importance of computational approaches in contributing to better decision-making, particularly in intricate logistics network optimization challenges.
To efficiently plan the coverage path of UAVs during the visual inspection of building facades, the task is divided into two parts: viewpoint planning and path planning. First, a K-medoid clustering method is proposed...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
To efficiently plan the coverage path of UAVs during the visual inspection of building facades, the task is divided into two parts: viewpoint planning and path planning. First, a K-medoid clustering method is proposed to generate the initial set of candidate viewpoints based on the point cloud model of the building facade. A greedy optimization algorithm is then employed to select the optimal subset of viewpoints, addressing the viewpoint planning problem. Second, an incomplete graph is constructed, and the objective function is defined for path planning, which is solved using the Gaussian Zenith-Ant Colony Optimization (GZ-ACO) algorithm. The path length and turning angles are considered optimization objectives. Simulation results demonstrate that the proposed viewpoint planning method reduces the number of viewpoints by 66.67% and 98.67%, respectively, compared to the iterative random sampling method and the displacement method. Furthermore, the GZ-ACO algorithm outperforms the traditional ant colony algorithm, achieving a 19.97% reduction in the objective function value and a 21.76% decrease in the average steering angle. The validity and feasibility of the proposed method are thus verified.
Digitalization, networking and intelligent management of Chinese language teaching resources are of great significance for improving teaching efficiency and promoting cultural exchange. However, in the face of massive...
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ISBN:
(数字)9798331536169
ISBN:
(纸本)9798331536176
Digitalization, networking and intelligent management of Chinese language teaching resources are of great significance for improving teaching efficiency and promoting cultural exchange. However, in the face of massive Chinese language teaching resources, the traditional classification method has been difficult to adapt to the characteristics of huge resources, diverse types and complex content. Because of its unique advantages in dealing with uncertainty and fuzziness, fuzzy clustering algorithm is applied to the design of this system to realize the intelligent and automatic classification of Chinese language teaching resources. The system architecture design includes four core parts: front-end user interface layer, back-end service layer, data processing layer and database storage layer. It adopts modular design, supports front-end separation mode and interacts through API. The functional modules of the system are divided into data preprocessing module, fuzzy clustering algorithm module, result display module, user management module and system maintenance module to ensure the maintainability, expansibility and efficiency of the system. In this study, the fuzzy C-means (FCM) algorithm is selected as the core algorithm, and the optimal clustering of data points and the center of each cluster are found by iteratively optimizing the objective function. The system test and evaluation show that FCM algorithm has achieved good clustering effect in the merging and classification of digital Chinese teaching resources, with contour coefficient of 0.75 and ARI index of 0.82, which shows high accuracy. The performance test results also show that the system has good response speed and stability, the throughput reaches 1050 requests per second, and the average utilization rate of CPU and memory is 57.3% and 68.9% respectively, which meets the requirements of high concurrency and big data processing. This system has high practicability and value in practical application, which ca
All coefficients interval number linear programming model with equality constrains and its solution method are discussed in this paper. A new solution method is presented to solve the all coefficients interval number ...
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All coefficients interval number linear programming model with equality constrains and its solution method are discussed in this paper. A new solution method is presented to solve the all coefficients interval number linear programming model containing not only inequality constrains but also strict equality constrains. The characteristic of the method is via the optimal value of deterministic linear programming to get the equality constrains to satisfy the decision maker's preference. With the method, the decision maker can obtain a satisfactory solution. Finally, the application value of the method is given by a portfolio investment case. And the result shows that the method is scientific and feasible.
In this paper we introduce a new parameterized Quadratic Decision Rule (QDR), a generalisation of the commonly employed Affine Decision Rule (ADR), for two-stage linear adjustable robust optimization problems with ell...
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Due to a volatile and challenging business environment, it is becoming more and more important for companies to secure their competitiveness through the efficient use of their resources in the global production networ...
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