1 Optimization Problems and Algorithms
1.1 Introduction
1.2 Optimization Problems
1.2.1 Continuous Optimization Problems
1.2.2 Combinatorial Optimization Problems
1.3 Exact Optimization Algorithms
1.3.1 Gradient-Based Algorithms
1.3.2 Linear Programming Algorithm
1.3.3 Branch-and-Bound
1.3.4 Dynamic Programming
1.4 Heuristic Optimization Algorithms
1.4.1 Genetic Algorithms
1.4.2 Simulated Annealing
1.4.3 Ant Colony Optimization
1.4.4 Particle Swarm Optimization
1.4.5 Differential Evolution
1.4.6 Harmony Search
1.4.7 Fireworks Algorithm
1.4.8 Water Wave Optimization
1.5 Summary
References
2 Biogeography-Based Optimization
2.1 Introduction
2.2 Background of Biogeography
2.3 The Basic Biogeography-Based Optimization Algorithm
2.3.1 The Migration Operator
2.3.2 The Mutation Operator
2.3.3 The Algorithmic Framework
2.3.4 Comparison with Some Classical Heuristics
2.4 Recent Advances of Biogeography-Based Optimization
2.4.1 Improved Biogeography-Based Optimization Algorithms
2.4.2 Adaption of BBO for Constrained Optimization
2.4.3 Adaption of BBO for Multi-objective Optimization
2.4.4 Adaption of BBO for Combinatorial Optimization
2.5 Summary
References
3 Localized Biogeography-Based Optimization: Enhanced by Local Topologies
3.1 Introduction
3.2 Population Topology
3.2.1 Global Topology
3.2.2 Local Topologies
3.2.3 Research of Heuristic Algorithms with Local Topologies
3.3 Localized Biogeography-Based Optimization Algorithms
3.3.1 Local-BBO with the Ring Topology
3.3.2 Local-BBO with the Square Topology
3.3.3 Local-BBO with the Random Topology
3.4 Computational Experiments
3.5 Summary
References
4 Ecogeography-Based Optimization: Enhanced by Ecogeographic Barriers and Differentiations
4.1 Introduction
4.2 Background of Ecogeography
4.3 The Ecogeography-Based Optimization Algorithm
4.3.1 Local Migration and Global Migration
4.3.2 Migration Based on Maturity
4.3.3 The Algorithmic Framework of EBO
4.4 Computational Experiments
4.4.1 Experimental Settings
4.4.2 Impact of the Immaturity Index η
4.4.3 Comparison of the 10-D Functions
4.4.4 Comparison of the 30-D Functions
4.4.5 Comparison of the 50-D Functions
4.4.6 Discussion
4.5 Summary
References .
5 Hybrid Biogeography-Based Optimization Algorithms
5.1 Introduction
5.2 Hybridization with Differential Evolution
5.2.1 The DE/BBO Algorithm
5.2.2 Local-DE/BBO
5.2.3 Self-adaptive DE/BBO
5.3 Hybridization with Harmony Search
5.3.1 Biogeographic Harmony Search
5.3.2 Computational Experiments
5.4 Hybridization with Fireworks Algorithm
5.4.1 A Hybrid BBO and FWA Algorithm
5.4.2 Computational Experiments
5.5 Summary
References
6 Application of Biogeography-Based Optimization in Transportation
6.1 Introduction
6.2 BBO for General Transportation Planning
6.2.1 A General Transportation Planning Problem
6.2.2 BBO Algorithms for the Problem
6.2.3 Computational Experiments
6.3 BBO for Emergency Transportation Planning
6.3.1 An Emergency Transportation Planning Problem
6.3.2 A BBO Algorithm for the Problem
6.3.3 Computational Experiments
6.4 BBO for Emergency Railway Wagon Scheduling
6.4.1 An Emergency Railway Wagon Scheduling Problem
6.4.2 A Hybrid BBO/DE Algorithm for the Problem
6.4.3 Computational Experiments
6.5 BBO for Emergency Air Transportation
6.5.1 An Emergency Air Transportation Problem
6.5.2 BHS and EBO Algorithms for the Problem
6.5.3 Computational Experiments
6.6 Summary
References
7 Application of Biogeography-Based Optimization in Job Scheduling
7.1 Introduction
7.2 BBO for Flow-Shop Scheduling
7.2.1 Flow-Shop Scheduling Problem
7.2.2 A BBO Algorithm for FSP
7.2.3 Computational Experiments
7.3 BBO for Job-Shop Scheduling
7.3.1 Job-Shop Scheduling Problem
7.3.2 An Enhanced BBO Algorithm for the Problem
7.3.3 Computational Experiments
7.4 BBO for Maintenance Job Assignment and Scheduling
7.4.1 A Maintenance Job Assignment and Scheduling Problem
7.4.2 A Multi-objective BBO Algorithm for the Problem
7.4.3 Computational Experiments
7.5 BBO for University Course Timetabling
7.5.1 A University Course Timetabling Problem
7.5.2 A Discrete EBO Algorithm for UCTP
7.5.3 Computational Experiments
7.6 Summary
References
8 Application of Biogeography-Based Optimization in Image Processing
8.1 Introduction
8.2 BBO for Image Compression
8.2.1 Fractal Image Compression
8.2.2 BBO Algorithms for Fractal Image Compression
8.2.3 Computational Experiments
8.3 BBO for Salient Object Detection
8.3.1 Salient Object Detection
8.3.2 BBO Algorithms for Salient Object Detection
8.3.3 Computational Experiments
8.4 BBO for Image Segmentation
8.4.1 Image Segmentation
8.4.2 The Proposed Hybrid BBO-FCM Algorithm
8.4.3 Computational Experiments
8.5 Summary
References
9 Biogeography-Based Optimization in Machine Learning
9.1 Introduction
9.2 BBO for ANN Parameter Optimization
9.2.1 The Problem of ANN Parameter Optimization
9.2.2 BBO Algorithms for ANN Parameter Optimization
9.2.3 Computational Experiments
9.3 BBO for ANN Structure and Parameter Optimization
9.3.1 The Problem of ANN Structure and Parameter Optimization
9.3.2 BBO Algorithms for ANN Structure and Parameter Optimization
9.3.3 Computational Experiments
9.4 BBO for Fuzzy Neural Network Training
9.4.1 The Problem of FNN Training
9.4.2 An EBO Algorithm for FNN Parameter Optimization
9.4.3 Computational Experiments
9.5 BBO for Deep Neural Network Optimization
9.5.1 The Problem of DNN Training
9.5.2 An EBO Algorithm for DNN Structure and Parameter Optimization
9.5.3 Computational Experiments
9.6 Summary
References

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。