生物地理学优化是生物启发计算中的一种新兴算法。本书介绍了生物地理学优化的背景、基本框架、主要操作和其它基本特征。特别的，本书介绍了作者及其研究团队对生物地理学优化的两个重要改进，生物地理学优化算法与其它启发式算法的融合，以及算法在交通运输、作业调度、图像处理、神经网络训练等多个领域的应用成果。本书内容对生物地理学优化以及生物启发计算的研究和应用均有重要的促进作用。

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Contents
1 Optimization Problems and Algorithms 1
1.1 Introduction 1
1.2 Optimization Problems 2
1.2.1 Continuous Optimization Problems 2
1.2.2 Combinatorial Optimization Problems 5
1.3 Exact Optimization Algorithms 7
1.3.1 Gradient-Based Algorithms 7
1.3.2 Linear Programming Algorithm 8
1.3.3 Branch-and-Bound 9
1.3.4 Dynamic Programming 11
1.4 Heuristic Optimization Algorithms 12
1.4.1 Genetic Algorithms 12
1.4.2 Simulated Annealing 14
1.4.3 Ant Colony Optimization 16
1.4.4 Particle Swarm Optimization 17
1.4.5 Differential Evolution 18
1.4.6 Harmony Search 19
1.4.7 Fireworks Algorithm 21
1.4.8 Water Wave Optimization 22
1.5 Summary 24
References 24
2 Biogeography-Based Optimization 27
2.1 Introduction 27
2.2 Background of Biogeography 27
2.3 The Basic Biogeography-Based Optimization Algorithm 32
2.3.1 The Migration Operator 32
2.3.2 The Mutation Operator 33
2.3.3 The Algorithmic Framework 34
2.3.4 Comparison with Some Classical Heuristics 35
2.4 Recent Advances of Biogeography-Based Optimization 36
2.4.1 Improved Biogeography-Based Optimization Algorithms 36
2.4.2 Adaption of BBO for Constrained Optimization 40
2.4.3 Adaption of BBO for Multi-objective Optimization 43
2.4.4 Adaption of BBO for Combinatorial Optimization 45
2.5 Summary 47
References 47
3 Localized Biogeography-Based Optimization: Enhanced by Local Topologies 51
3.1 Introduction 51
3.2 Population Topology 51
3.2.1 Global Topology 51
3.2.2 Local Topologies 53
3.2.3 Research of Heuristic Algorithms with Local Topologies 56
3.3 Localized Biogeography-Based Optimization Algorithms 57
3.3.1 Local-BBO with the Ring Topology 57
3.3.2 Local-BBO with the Square Topology 58
3.3.3 Local-BBO with the Random Topology 58
3.4 Computational Experiments 61
3.5 Summary 66
References 66
4 Ecogeography-Based Optimization: Enhanced by Ecogeographic Barriers and Differentiations 69
4.1 Introduction 69
4.2 Background of Ecogeography 69
4.3 The Ecogeography-Based Optimization Algorithm 71
4.3.1 Local Migration and Global Migration 71
4.3.2 Migration Based on Maturity 72
4.3.3 The Algorithmic Framework of EBO 72
4.4 Computational Experiments 73
4.4.1 Experimental Settings 73
4.4.2 Impact of the Immaturity Index η 74
4.4.3 Comparison of the 10-D Functions 74
4.4.4 Comparison of the 30-D Functions 78
4.4.5 Comparison of the 50-D Functions 83
4.4.6 Discussion 83
4.5 Summary 86
References .87
5 Hybrid Biogeography-Based Optimization Algorithms 89
5.1 Introduction 89
5.2 Hybridization with Differential Evolution 89
5.2.1 The DE/BBO Algorithm 89
5.2.2 Local-DE/BBO 91
5.2.3 Self-adaptive DE/BBO 97
5.3 Hybridization with Harmony Search 104
5.3.1 Biogeographic Harmony Search 104
5.3.2 Computational Experiments 105
5.4 Hybridization with Fireworks Algorithm 109
5.4.1 A Hybrid BBO and FWA Algorithm 109
5.4.2 Computational Experiments 110
5.5 Summary 114
References 114
6 Application of Biogeography-Based Optimization in Transportation 117
6.1 Introduction 117
6.2 BBO for General Transportation Planning 117
6.2.1 A General Transportation Planning Problem 117
6.2.2 BBO Algorithms for the Problem 119
6.2.3 Computational Experiments 119
6.3 BBO for Emergency Transportation Planning 123
6.3.1 An Emergency Transportation Planning Problem 123
6.3.2 A BBO Algorithm for the Problem 124
6.3.3 Computational Experiments 125
6.4 BBO for Emergency Railway Wagon Scheduling 127
6.4.1 An Emergency Railway Wagon Scheduling Problem 128
6.4.2 A Hybrid BBO/DE Algorithm for the Problem 131
6.4.3 Computational Experiments 134
6.5 BBO for Emergency Air Transportation 137
6.5.1 An Emergency Air Transportation Problem 137
6.5.2 BHS and EBO Algorithms for the Problem 139
6.5.3 Computational Experiments 139
6.6 Summary 140
References 141
7 Application of Biogeography-Based Optimization in Job Scheduling 143
7.1 Introduction 143
7.2 BBO for Flow-Shop Scheduling 143
7.2.1 Flow-Shop Scheduling Problem 143
7.2.2 A BBO Algorithm for FSP 146
7.2.3 Computational Experiments 147
7.3 BBO for Job-Shop Scheduling 149
7.3.1 Job-Shop Scheduling Problem 149
7.3.2 An Enhanced BBO Algorithm for the Problem 151
7.3.3 Computational Experiments 153
7.4 BBO for Maintenance Job Assignment and Scheduling 156
7.4.1 A Maintenance Job Assignment and Scheduling Problem 156
7.4.2 A Multi-objective BBO Algorithm for the Problem 158
7.4.3 Computational Experiments 160
7.5 BBO for University Course Timetabling 163
7.5.1 A University Course Timetabling Problem 163
7.5.2 A Discrete EBO Algorithm for UCTP 166
7.5.3 Computational Experiments 169
7.6 Summary 173
References 173
8 Application of Biogeography-Based Optimization in Image Processing 177
8.1 Introduction 177
8.2 BBO for Image Compression 177
8.2.1 Fractal Image Compression 177
8.2.2 BBO Algorithms for Fractal Image Compression 180
8.2.3 Computational Experiments 180
8.3 BBO for Salient Object Detection 181
8.3.1 Salient Object Detection 181
8.3.2 BBO Algorithms for Salient Object Detection 184
8.3.3 Computational Experiments 184
8.4 BBO for Image Segmentation 187
8.4.1 Image Segmentation 187
8.4.2 The Proposed Hybrid BBO-FCM Algorithm 191
8.4.3 Computational Experiments 192
8.5 Summary 196
References 197
9 Biogeography-Based Optimization in Machine Learning 199
9.1 Introduction 199
9.2 BBO for ANN Parameter Optimization 199
9.2.1 The Problem of ANN Parameter Optimization 199
9.2.2 BBO Algorithms for ANN Parameter Optimization 201
9.2.3 Computational Experiments 202
9.3 BBO for ANN Structure and Parameter Optimization 203
9.3.1 The Problem of ANN Structure and Parameter Optimization 203
9.3.2 BBO Algorithms for ANN Structure and Parameter Optimization 204
9.3.3 Computational Experiments 205
9.4 BBO for Fuzzy Neural Network Training 207
9.4.1 The Problem of FNN Training 207
9.4.2 An EBO Algorithm for FNN Parameter Optimization 210
9.4.3 Computational Experiments 211
9.5 BBO for Deep Neural Network Optimization 212
9.5.1 The Problem of DNN Training 212
9.5.2 An EBO Algorithm for DNN Structure and Parameter Optimization 214
9.5.3 Computational Experiments 215
9.6 Summary 215
References 216