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Jingdong book

群体智能算法及其应用

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Jingdong book

群体智能算法及其应用

Name: 群体智能算法及其应用
Brand: Jingdong book
SKU: 3118002631
Price: 46.77 USD
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Content Description

本书以人工鱼群算法、烟花爆炸优化算法两个典型的群体智能算法为主，系统介绍了算法的原理，建立了基于协作、竞争机制的群体智能算法的数学模型。全书着重分析了人工鱼群算法和烟花爆炸优化算法的弱点，并提出了多种新颖的改进机制，给出了算法的详细实现步骤。本书还详细探讨了部分群体智能算法在VRP问题、图像边缘检测、SVM反问题、网络态势预测、数据聚类、特征选择等领域内的应用，并介绍了近年来出现的两个比较新颖的群体智能算法，顾问引导搜索算法和教―学优化算法。

Author Description

王培崇，石家庄经济学院信息工程学院副教授。主要研究方向为：模式识别、机器学习、人工智能等方面。发表了多篇论文，其中核心期刊论文10篇。

Catalogue

目录
第1章群体智能算法概述 1
1．1 群体智能算法的特点 1
1．1．1 智能性 1
1．1．2 隐含本质并行性 2
1．1．3 解的近似性 2
1．2 群体智能算法的计算模式 2
1．2．1 社会协作机制 3
1．2．2 自我适应机制 3
1．2．3 竞争机制 4
1．3 遗传算法 4
1．3．1 标准遗传算法原理 5
1．3．2 编码机制与主要算子 7
1．4 差异演化算法 8
1．5 粒子群算法 10
1．5．1 粒子群算法的原理 10
1．5．2 PSO算法的计算模型 11
1．6 教―学优化算法 13
1．7 顾问引导搜索算法 13
1．8 本章小结 15
参考文献 16
第2章人工鱼群算法 18
2．1 人工鱼群算法的数学模型 18
2．2 人工鱼群算法的收敛性分析 21
2．2．1 常用距离 21
2．2．2 基于Markfov链技术的收敛性分析 22
2．2．3 基于压缩映射定理的收敛性分析 25
2．3 人工鱼群算法的相关研究 26
2．3．1 参数的改进 27
2．3．2 与其他智能算法的融合 28
2．3．3 其他的改进方法 29
2．4 本章小结 32
参考文献 32
第3章人工鱼群算法的改进研究 34
3．1 小生境人工鱼群算法 34
3．1．1 小生境技术 34
3．1．2 算法实现 36
3．1．3 算法的收敛性 36
3．1．4 仿真实验与分析 38
3．1．5 结论 40
3．2 自适应人工鱼群算法 40
3．2．1 参数自适应机制 40
3．2．2 算法实现 42
3．2．3 仿真实验与分析 42
3．2．4 结论 44
3．3 基于种群分类的人工鱼群算法 44
3．3．1 种群分类思想及设置 45
3．3．2 算法实现 46
3．3．3 仿真实验与分析 47
3．3．4 结论 50
3．4 混和反向学习人工鱼群算法 50
3．4．1 反向学习 50
3．4．2 佳点集 51
3．4．3 人工鱼群算法的改进机制 51
3．4．4 仿真实验与分析 54
3．4．5 结论 59
3．5 精英竞争人工鱼群算法 59
3．5．1 基于动态随机搜索的精英训练 59
3．5．2 算法实现 60
3．5．3 仿真实验与分析 61
3．5．4 结论 67
3．6 随机游走人工鱼群算法 67
3．6．1 Lévy Flight机制 67
3．6．2 算法改进思想 68
3．6．3 算法实现 69
3．6．4 仿真实验与分析 70
3．6．5 结论 72
3．7 混合群搜索人工鱼群算法 73
3．7．1 标准群搜索优化算法 73
3．7．2 群搜索优化算法的改进 75
3．7．3 混合群搜索人工鱼群算法 77
3．7．4 仿真实验与分析 78
3．7．5 结论 81
3．8 本章小结 81
参考文献 82
第4章烟花爆炸优化算法及改进 83
4．1 烟花爆炸优化算法 83
4．2 混沌烟花爆炸优化算法 86
4．2．1 混沌搜索算法 86
4．2．2 算法实现 87
4．2．3 仿真实验与分析 87
4．2．4 结论 91
4．3 混合动态搜索烟花爆炸优化算法 91
4．3．1 算法实现 91
4．3．2 仿真实验与分析 92
4．3．3 结论 96
4．4 混合反向学习烟花爆炸优化算法 96
4．4．1 精英反向学习 96
4．4．2 基于模拟退火机制的种群选择 97
4．4．3 算法实现 97
4．4．4 仿真实验与分析 98
4．4．5 结论 102
4．5 随机游走烟花爆炸优化算法 102
4．5．1 基于随机游走机制的变异算子 103
4．5．2 基于Boltzmann 子个体选择 103
4．5．3 算法实现 104
4．5．4 仿真实验与分析 105
4．5．5 结论 109
4．6 本章小结 109
参考文献 109
第5章群体智能算法的应用 110
5．1 物流配送中的车辆调度问题 110
5．1．1 问题的提出 110
5．1．2 组合优化 111
5．1．3 车辆调度问题的数学模型 111
5．1．4 求解VRP的混合人工鱼群遗传算法 112
5．1．5 仿真实验结果 113
5．2 求解SVM反问题的差异演化算法 113
5．2．1 问题的提出 113
5．2．2 差异演化算法的设计 114
5．2．3 差异演化算法的改进 114
5．2．4 仿真实验结果 116
5．3 求解聚类问题的人工鱼群算法 118
5．3．1 聚类模型 118
5．3．2 算法的设计 119
5．3．3 算法实现 120
5．3．4 仿真实验结果 121
5．4 求解测试用例自动化问题的人工鱼群算法 123
5．4．1 路径测试模型 123
5．4．2 混沌搜索 125
5．4．3 算法的设计 125
5．4．4 仿真实验结果 127
5．5 求解关联规则挖掘的差异演化算法 129
5．5．1 规则挖掘 129
5．5．2 算法的设计 131
5．5．3 仿真实验结果 133
5．6 求解特征选择的人工鱼群算法 136
5．6．1 特征选择 136
5．6．2 算法的设计 136
5．6．3 仿真实验结果 137
5．7 求解网络安全态势预测的人工鱼群算法 139
5．7．1 网络安全态势预测模型 140
5．7．2 算法的设计 141
5．7．3 仿真实验结果 143
5．8 求解图像边缘检测的遗传算法 146
5．8．1 数字图像边缘 146
5．8．2 Sobel边缘检测算子 148
5．8．3 面向图像边缘检测的遗传算法 149
5．8．4 仿真实验结果 151
5．8．5 结论 155
5．9 本章小结 155
参考文献 157
第6章总结与展望 159

Book Abstract

《群体智能算法及其应用》：
将部分优秀个体所组成的种群称为精英咱群，则该种群中的个体必然包含了更多的引导种群全局收敛的有益信息。如果算法最终收敛到全局最佳，则精英种群所形成的搜索区域肯定会收敛到全局最佳所在的搜索区域，所以充分利用精英种群的信息，搜索精英种群内个体的反向解所在空间，将会提高算法的收敛速度，改善算法的全局收敛能力。
为了保留运算过程中产生的优化结果信息，对个体所在边界采用动态机制。
基于以上分析，在AFSA的每次迭代过程中对由部分优秀个体所组成的精英种群执行一般动态反向学习，并将所生成的反向种群与原种群一起进行优胜劣汰，选择其中POP个优质个体组成子种群，进入下一次迭代。
根据鱼群算法的原理可知，随着计算过程的进行，种群将会逐渐聚集在一起，种群内个体的适应度值会接近种群的平均适应度值。应用参数来表征鱼群的聚集程度，易知a的值越小，聚集程度越高，代表了种群或者聚集于全局最优，或者聚集于局部最优，种群多样性趋同。当然，对于a 所代表的情况难以区分，故在迭代过程中通过延缓种群多样性下降的方法来保证算法的全局收敛能力。设阈值λ为较小的值，当a≤λ时，除保留10％的优秀个体外，其余在解空间内采用佳点集进行重新初始化。
算法3.4反向学习人工鱼群算法（OBLAFSA）
设待求解目标函数为f（x），即鱼个体所处位置的食物浓度，个体的状态表示为x=（x1，x2，…，xa）。
输入：规模为POP的种群。
输出：最佳个体Xgbest。
步骤1.初始化算法的参数。
步骤2.应用佳点集机制进行种群初始化，并计算全部个体的适应度值。
步骤3.自由游动算子。默认状态下种群的个体Xi（f）在各自的视野visual范围内进行随机游动一步长step。
步骤4.觅食行为算子。
……

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