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Wind Algorithm Optimization a New Novel Technique for Global Optimization Problems

作 者: Muhammad Rauf
导 师: Yin Minghao
学 校: 东北师范大学
专 业: Computer Application Technology
关键词: Global Optimization Evolutionary algorithm Swarm Intelligence Pressure Gradient Force Coriolis Effect
分类号: TP301.6
类 型: 硕士论文
年 份: 2013年
下 载: 1次
引 用: 0次
阅 读: 论文下载
 

内容摘要


In the past few decades a lot of work has been done in the field of Optimization. Different kinds of algorithm have been proposed. Many of these methods are inspired by the natural phenomenon. In the dissertation, a new algorithm inspired by the environmental phenomena is introduced, which is called wind algorithm for optimization (WAO). This algorithm is based on the natural wind flow phenomenon from high to low pressure area, different gas molecules and lots of dust particle travel with in it. The proposed method involves simple techniques and equations to guide the particle to find the optimal solution. A set of benchmark functions is used to demonstrate the performance of the propose method and compare with well-known methods. The experiments show that WAO have tremendous capability to explore the search space to find the global optimization. The results obtained demonstrate the high performance of WAO in solving different kind of functions.

全文目录


Abstract  4-5
Acknowledgment  5-11
Chapter 1 Introduction  11-14
  1.1. Optimization  11-13
  1.2. Structure of the Thesis  13-14
Chapter 2 Background  14-23
  2.1. Overview  14-15
  2.2. Deterministic Algorithms  15
  2.3. Probabilistic Algorithms  15-16
  2.4. Evolutionary algorithms  16-20
  2.5. Swarm intelligence  20-23
Chapter 3 Flow of the Wind  23-29
  3.1. Introduction  23-24
  3.2. Pressure Gradient Force (PGF)  24-27
  3.3. Coriolis Effect  27-29
Chapter 4 The Algorithm: WAO  29-38
  4.1. Introduction  29-32
  4.2. Major Variables  32-33
    4.2.1 Pressure (P)  32
    4.2.2. Pressure Difference (△p)  32
    4.2.3. Velocity (v)  32-33
    4.2.4. Position(x)  33
    4.2.5. Mass(m)  33
    4.2.6. Coriolis Force (C)  33
  4.3. Major steps of algorithm  33-38
    4.3.1. Step 1: [initialization]  33-34
    4.3.2. Step 2: [Initial Fitness Value]  34
    4.3.3. Step 3: [Set Pressure]  34-35
    4.3.4. Step 4: [Set Mass]  35
    4.3.5. Step 5: [Update Velocity and Position]  35-36
    4.3.6. Step 6: [Update Pressure]  36
    4.3.7. Step 7: [Update Mass]  36
    4.3.8. Step 8: [Update Position of Worst Particle]  36
    4.3.9. Step 9: [Fitness evaluation]  36
    4.3.10. Step 10: [Stopping criteria]  36-38
Chapter 5 Analysis and Results  38-57
  5.1. Evaluation Procedure  38-41
  5.2. Results  41-57
    5.2.1. Two Dimensional Functions  41-44
    5.2.2. Multi-dimensional Functions (10 and 20)  44-51
    5.2.3. 30 Dimensional Search Space  51-57
Conclusions  57-58
References  58-64

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