臺灣博碩士論文加值系統

本研究提出改良型複製免疫演算法 (Modified Clonal Selection Algorithm, MCSA)。在之前的研究中，複製免疫演算法(Clonal Selection Algorithm, CSA)是能平行處理的演算法，並且能改善傳統遺傳基因演算法(Genetic Algorithm, GA)中早熟與局部最佳解的問題；但複製免疫演算法仍有兩項可改進的缺點：缺乏彈性的突變率和單調的突變法則。
因此在本研究中，我們提出附有自適性突變策略的改良型複製免疫演算法， 在新的突變策略中，以抗體本身親和力為依據，自適性調整突變率的大小值，並輔以改良的複製細胞群架構與不同的突變運算子，來改善原始複製演算法的尋優效率與收斂速度，並由實驗結果可得，改良型複製免疫演算法能有效應用在數值問題、影像雜訊消除與通信功率頻寬資源分配的工程問題上。

In this thesis, a modified clonal selection algorithm (MCSA) is presented. Formerly, the standard clonal selection algorithm (CSA) is highly parallel and improves the premature convergence and local search problems in traditional genetic algorithm. However, there are drawbacks in clonal selection algorithm: the lack of flexible mutation probability and monotonous mutation mode.
Therefore, we propose a modified clonal selection algorithm (MCSA) which has an adaptive maturation strategy based on affinity and clone framework to search approximate optimal solution. The simulation results show that the modified clonal selection algorithm has better efficiency and convergence than the standard clonal selection algorithm in the mathematical optimization problems, image noise cancellation, and communication networks power/rate resource optimization, respectively.

Abstract I
Contents IV
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Brief sketch of the thesis 3
Chapter 2 Biological Immune System 4
2.1 Introduction 4
2.2 Structure of Immunity 5
2.2.1 Lymphocytes 6
2.2.2 The B cell 6
2.2.3 The T cell 7
2.2.4 The Antibody 8
2.3 Phenomenon of Immunity 9
2.3.1 Primary and Secondary Immune Responses 9
2.3.2 Clonal Selection 10
2.4 Artificial Immune Systems 11
Chapter 3 Modified Clonal Selection Algorithm 13
3.1 Clonal Selection Theory 13
3.2 Clonal Selection Algorithm 14
3.3 Causations of Improving 17
3.4 Modified Clonal Selection Algorithm 18
3.5 The Maturation Strategy 20
3.5.1 The Pyramid Framework 21
3.5.2 Self-Adaption Mutation Probability 21
3.6 The Mutation Operators 23
3.6.1 Gaussian Mutation 23
3.6.2 Swapping Mutation 25
3.6.3 Multi-Point Mutation 25
3.7 The Primary and Secondary Immune Response Mechanism 26
Chapter 4 Mathematical Optimization Problems 29
4.1 Introduction 29
4.2 Problem Definitions and Results 29
4.3 Summary 34
Chapter 5 Image Noise Cancellation 35
5.1 Introduction 35
5.2 Cellular Neural Network 36
5.3 MCSA-CNN Templates Optimization 37
5.4 Simulation Results 40
5.5 Summary 44
Chapter 6 Communication Network Resource Optimization 45
6.1 Introduction 45
6.2 Third Generation Network 45
6.3 Mathematical Formulation of Resources Control 47
6.4 Experimental Results 50
6.5 Summary 55
Chapter 7 Conclusions 59
References 61
List of Publications 64
Biography 65

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