The Optimization of Carbon Fiber Drawing Process Based on Cooperative Immune Clonal Selection Algorithm

2013 ◽  
Vol 681 ◽  
pp. 304-308 ◽  
Author(s):  
Jia Jia Chen ◽  
Yong Sheng Ding ◽  
Kuang Rong Hao
Keyword(s):  
Carbon Fiber ◽  
Optimization Problem ◽  
Clonal Selection ◽  
Convergence Time ◽  
Clonal Selection Algorithm ◽  
Drawing Process ◽  
Multi Objective Optimization ◽  
Selection Algorithm ◽  
Elongation Ratio ◽  
Optimal Linear

Drawing is an important process during carbon fiber production. How to obtain the fittest drawing ratios distribute scheme is a typical multi-objective optimization problem. We propose a novel cooperative immune clonal selection algorithm (CICSA) to obtain the optimal linear density and breaking elongation ratio. The CICSA features in synergetic evolution, clonal operation and mutation operation. Compared with the immune algorithm and the genetic algorithm, it has the best performance in precision and convergence time.

The Application of an Immune Clonal Selection Algorithm Based on the Information Entropy in Truss Structure Multi-Objective Optimization

2012 ◽  
Vol 249-250 ◽  
pp. 1119-1125
Author(s):  
Chang Yuan Hu ◽  
He Sheng Tang ◽  
Li Xin Deng ◽  
Song Tao Xue
Keyword(s):  
Information Entropy ◽  
Optimization Design ◽  
Clonal Selection ◽  
Minimum Weight ◽  
Truss Structures ◽  
Penalty Function Method ◽  
Clonal Selection Algorithm ◽  
Multi Objective Optimization ◽  
Selection Algorithm ◽  
Multi Objective

In order to solve the conflict multi-objective optimization of truss structures between the structure minimum weight and safety redundancy, the immune clonal selection algorithm based on information entropy was adopted in this paper. Based on the immunology theory, the non-dominated neighbor-based selection, proportional cloning and elitism strategy were introduced in the multi-objective immune clonal selection algorithm (MOICSA) to enhance the diversity, the uniformity and the convergence of the obtained solution. Mathematical models for truss multi-objective optimization design are constructed, in which the information entropy value of bar stress is taken as one of objective functions, and penalty function method was used to deal with violated constraints. Several classical problems are solved using the MOICSA algorithm, and the results compared with other optimization methods. The simulation results show that the method can achieve the effect of multiple-objective optimization successfully.

Feature Selection Based on Clonal Selection Algorithm

2011 ◽  
pp. 180-203
Author(s):  
Xiangrong Zhang ◽  
Fang Liu
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Clonal Selection ◽  
Good Choice ◽  
Affinity Maturation ◽  
Clonal Selection Algorithm ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Rapid Convergence ◽  
Optimal Feature Subset

The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on. Feature selection is usually used to achieve the same or better performance using fewer features. It can be considered as an optimization problem and aims to find an optimal feature subset from the available features according to a certain criterion function. Clonal selection algorithm is a good choice in solving an optimization problem. It introduces the mechanisms of affinity maturation, clone, and memorization. Rapid convergence and good global searching capability characterize the performance of the corresponding operations. In this study, the property of rapid convergence to global optimum of clonal selection algorithm is made use of to speed up the searching of the most appropriate feature subset among a huge number of possible feature combinations. Compared with the traditional genetic algorithm-based feature selection, the clonal selection algorithm-based feature selection can find a better feature subset for classification. Experimental results on datasets from UCI learning repository, 16 types of Brodatz textures classification, and synthetic aperture radar (SAR) images classification demonstrated the effectiveness and good performance of the method in applications.

Reactive Power Optimization Based on Immune Clonal Selection Algorithm

2014 ◽  
Vol 1030-1032 ◽  
pp. 1751-1754
Author(s):  
Zheng Yuan Li ◽  
Gang Zhang ◽  
Chao Li ◽  
Wei Zheng ◽  
Jing Jing Zheng
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Genetic Manipulation ◽  
Clonal Selection ◽  
Current Status ◽  
Clonal Selection Algorithm ◽  
Reactive Power Optimization ◽  
Selection Algorithm

Based on the full understanding of the current status of the reactive power optimization study, we propose an improved type of immune algorithm to solve the reactive power optimization problem by introducing the immune clonal selection algorithm (ICSA) genetic manipulation, affinity of mature, cloning and memory mechanism, and use the appropriate operator to ensure that the algorithm can quickly converge to the global optimal solution to improve the efficiency of the algorithm solving and solution accuracy, avoiding the "curse of dimensionality" and precocious problems. ICSA algorithm is proposed to improve the convergence speed simultaneously. Better maintain the diversity of the population. Effectively overcome the premature convergence of evolutionary computation itself is difficult to solve the problem. Four different examples of calculation results show that this method has superior computational efficiency and convergence capability, high quality and are solved, very suitable for solving large-scale power system reactive power optimization problem, with a strong practical value.

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