scholarly journals The Research of High Dimensional Solution Space Adaptation Based on Case-Based Reasoning during Mission Planning

2015 ◽  
Vol 05 (12) ◽  
pp. 454-463
Author(s):  
媛 张
Keyword(s):  
Solution Space ◽  
Mission Planning ◽  
High Dimensional ◽  
Case Based Reasoning ◽  
Dimensional Solution ◽  
Space Adaptation ◽  
Case Based

Extension Case-Based Reasoning for Product Configuration Design

2009 ◽  
Vol 69-70 ◽  
pp. 616-620 ◽  
Author(s):  
Yan Wei Zhao ◽  
F. Zhang ◽  
M.Y. Zhang ◽  
Jian Chen ◽  
N. Su
Keyword(s):  
Solution Space ◽  
Product Configuration ◽  
Configuration Design ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Final Configuration ◽  
Similarity Calculation ◽  
Overall Evaluation ◽  
Configuration Scheme ◽  
Case Based

The interface was regarded as standard and not considered in traditional configuration design, which made it difficult to apply to the existence product configuration. The paper proposes an extension case-based reasoning for product configuration design. With matter-elements, reasoning model of Extension Case-Based Reasoning (ECBR) is established, and its corresponding algorithm is proposed. During the configuration design, the solution space of configuration schemes is obtained by the similarity calculation, and then the overall evaluation of similarity and compatible degrees is adopted to form the final configuration scheme. A prototype system of reducer configuration design is successfully developed according to the method, and it proves the proposed method that is feasible and effective.

On the Optimal Decomposition of High-Dimensional Solution Spaces of Complex Systems

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Stefan Erschen ◽  
Fabian Duddeck ◽  
Matthias Gerdts ◽  
Markus Zimmermann
Keyword(s):  
Complete Solution ◽  
Cartesian Product ◽  
Solution Space ◽  
Linear Functions ◽  
High Dimensional ◽  
Development Work ◽  
Interior Point Algorithm ◽  
Dimensional Solution ◽  
Design Work ◽  
Design Variables

In the early development phase of complex technical systems, uncertainties caused by unknown design restrictions must be considered. In order to avoid premature design decisions, sets of good designs, i.e., designs which satisfy all design goals, are sought rather than one optimal design that may later turn out to be infeasible. A set of good designs is called a solution space and serves as target region for design variables, including those that quantify properties of components or subsystems. Often, the solution space is approximated, e.g., to enable independent development work. Algorithms that approximate the solution space as high-dimensional boxes are available, in which edges represent permissible intervals for single design variables. The box size is maximized to provide large target regions and facilitate design work. As a result of geometrical mismatch, however, boxes typically capture only a small portion of the complete solution space. To reduce this loss of solution space while still enabling independent development work, this paper presents a new approach that optimizes a set of permissible two-dimensional (2D) regions for pairs of design variables, so-called 2D-spaces. Each 2D-space is confined by polygons. The Cartesian product of all 2D-spaces forms a solution space for all design variables. An optimization problem is formulated that maximizes the size of the solution space, and is solved using an interior-point algorithm. The approach is applicable to arbitrary systems with performance measures that can be expressed or approximated as linear functions of their design variables. Its effectiveness is demonstrated in a chassis design problem.

scholarly journals Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection

2021 ◽  
Author(s):  
Xiaodong Ren ◽  
Daofu Guo ◽  
Zhigang Ren ◽  
Yongsheng Liang ◽  
An Chen
Keyword(s):  
Optimization Problems ◽  
Solution Space ◽  
Random Projection ◽  
Surrogate Models ◽  
High Dimensional ◽  
Local Models ◽  
Dimensional Solution ◽  
Training Samples ◽  
Low Dimensional ◽  
Expensive Optimization

AbstractBy remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems. The success of hierarchical SAEAs mainly profits from the potential benefit of their global surrogate models known as “blessing of uncertainty” and the high accuracy of local models. However, their performance leaves room for improvement on high-dimensional problems since now it is still challenging to build accurate enough local models due to the huge solution space. Directing against this issue, this study proposes a new hierarchical SAEA by training local surrogate models with the help of the random projection technique. Instead of executing training in the original high-dimensional solution space, the new algorithm first randomly projects training samples onto a set of low-dimensional subspaces, then trains a surrogate model in each subspace, and finally achieves evaluations of candidate solutions by averaging the resulting models. Experimental results on seven benchmark functions of 100 and 200 dimensions demonstrate that random projection can significantly improve the accuracy of local surrogate models and the new proposed hierarchical SAEA possesses an obvious edge over state-of-the-art SAEAs.

scholarly journals An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jian Zhang ◽  
Huanzhou Li ◽  
Zhangguo Tang ◽  
Qiuping Lu ◽  
Xiuqing Zheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cooperative Learning ◽  
Learning Strategy ◽  
Solution Space ◽  
High Dimensional ◽  
Multilevel Thresholding ◽  
Dimensional Solution ◽  
Quantum Genetic Algorithm ◽  
Adjustment Strategy ◽  
Adaptive Adjustment

A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.

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