Degree Reduction of S-λ Curves Using a Genetic Simulated Annealing Algorithm
Abstract
:1. Introduction
2. Definition of S-λ Curves
2.1. S-λ Distributions and Basis Functions
2.2. S-λ Curves
3. Degree Reduction of S-λ Curves Using an Intelligent Algorithm
3.1. The Degree Reduction Problem
3.2. Basic Principles of the Degree Reduction for S-λ Curve
3.2.1. Elementary Algorithms
3.2.2. Initialization of the Control Parameters and the Group
3.2.3. Selection of a Fitness Function
3.2.4. Selection
3.2.5. Crossover
3.2.6. Mutation
3.2.7. Termination Conditions
3.2.8. Setting Parameters
3.3. The Procedure of the Degree Reduction for S-λ Curve
3.4. Results and Discussions
3.4.1. Degree Reduction of S-λ Curves with Fixed Endpoints
3.4.2. Degree Reduction of S-λ Curves with Unconstrained Endpoint
3.4.3. Quantitative Evaluation of the Degree Reduction Algorithm
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Lu, J.; Qin, X. Degree Reduction of S-λ Curves Using a Genetic Simulated Annealing Algorithm. Symmetry 2019, 11, 15. https://doi.org/10.3390/sym11010015
Lu J, Qin X. Degree Reduction of S-λ Curves Using a Genetic Simulated Annealing Algorithm. Symmetry. 2019; 11(1):15. https://doi.org/10.3390/sym11010015
Chicago/Turabian StyleLu, Jing, and Xinqiang Qin. 2019. "Degree Reduction of S-λ Curves Using a Genetic Simulated Annealing Algorithm" Symmetry 11, no. 1: 15. https://doi.org/10.3390/sym11010015