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Biogeography-Based Optimization: Algorithms and Applications 2019 ed. [Kõva köide]

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  • Formaat: Hardback, 221 pages, kõrgus x laius: 235x155 mm, kaal: 524 g, 30 Illustrations, color; 20 Illustrations, black and white; XI, 221 p. 50 illus., 30 illus. in color., 1 Hardback
  • Ilmumisaeg: 04-Oct-2018
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811325855
  • ISBN-13: 9789811325854
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Biogeography-Based Optimization: Algorithms and Applications 2019 ed.Suurem pilt
  • Formaat: Hardback, 221 pages, kõrgus x laius: 235x155 mm, kaal: 524 g, 30 Illustrations, color; 20 Illustrations, black and white; XI, 221 p. 50 illus., 30 illus. in color., 1 Hardback
  • Ilmumisaeg: 04-Oct-2018
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811325855
  • ISBN-13: 9789811325854
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811325854.html

This book introduces readers to the background, general framework, main operators, and other basic characteristics of biogeography-based optimization (BBO), which is an emerging branch of bio-inspired computation. In particular, the book presents the authors’ recent work on improved variants of BBO, hybridization of BBO with other algorithms, and the application of BBO to a variety of domains including transportation, image processing, and neural network learning. The content will help to advance research into and application of not only BBO but also the whole field of bio-inspired computation. The algorithms and applications are organized in a step-by-step manner and clearly described with the help of pseudo-codes and flowcharts. The readers will learn not only the basic concepts of BBO but also how to apply and adapt the algorithms to the engineering optimization problems they actually encounter.

1 Optimization Problems and Algorithms
1(26)
1.1 Introduction
1(1)
1.2 Optimization Problems
2(5)
1.2.1 Continuous Optimization Problems
2(3)
1.2.2 Combinatorial Optimization Problems
5(2)
1.3 Exact Optimization Algorithms
7(5)
1.3.1 Gradient-Based Algorithms
7(1)
1.3.2 Linear Programming Algorithm
8(1)
1.3.3 Branch-and-Bound
9(2)
1.3.4 Dynamic Programming
11(1)
1.4 Heuristic Optimization Algorithms
12(12)
1.4.1 Genetic Algorithms
12(2)
1.4.2 Simulated Annealing
14(2)
1.4.3 Ant Colony Optimization
16(1)
1.4.4 Particle Swarm Optimization
17(1)
1.4.5 Differential Evolution
18(1)
1.4.6 Harmony Search
19(2)
1.4.7 Fireworks Algorithm
21(1)
1.4.8 Water Wave Optimization
22(2)
1.5 Summary
24(1)
References
24(3)
2 Biogeography-Based Optimization
27(24)
2.1 Introduction
27(1)
2.2 Background of Biogeography
27(5)
2.3 The Basic Biogeography-Based Optimization Algorithm
32(4)
2.3.1 The Migration Operator
32(1)
2.3.2 The Mutation Operator
33(1)
2.3.3 The Algorithmic Framework
34(1)
2.3.4 Comparison with Some Classical Heuristics
35(1)
2.4 Recent Advances of Biogeography-Based Optimization
36(11)
2.4.1 Improved Biogeography-Based Optimization Algorithms
36(4)
2.4.2 Adaption of BBO for Constrained Optimization
40(3)
2.4.3 Adaption of BBO for Multi-objective Optimization
43(2)
2.4.4 Adaption of BBO for Combinatorial Optimization
45(2)
2.5 Summary
47(1)
References
47(4)
3 Localized Biogeography-Based Optimization: Enhanced By Local Topologies
51(18)
3.1 Introduction
51(1)
3.2 Population Topology
51(6)
3.2.1 Global Topology
51(2)
3.2.2 Local Topologies
53(3)
3.2.3 Research of Heuristic Algorithms with Local Topologies
56(1)
3.3 Localized Biogeography-Based Optimization Algorithms
57(4)
3.3.1 Local-BBO with the Ring Topology
57(1)
3.3.2 Local-BBO with the Square Topology
58(1)
3.3.3 Local-BBO with the Random Topology
58(3)
3.4 Computational Experiments
61(5)
3.5 Summary
66(1)
References
66(3)
4 Ecogeography-Based Optimization: Enhanced by Ecogeographic Barriers and Differentiations
69(20)
4.1 Introduction
69(1)
4.2 Background of Ecogeography
69(2)
4.3 The Ecogeography-Based Optimization Algorithm
71(2)
4.3.1 Local Migration and Global Migration
71(1)
4.3.2 Migration Based on Maturity
72(1)
4.3.3 The Algorithmic Framework of EBO
72(1)
4.4 Computational Experiments
73(13)
4.4.1 Experimental Settings
73(1)
4.4.2 Impact of the Immaturity Index η
74(1)
4.4.3 Comparison of the 10-D Functions
74(4)
4.4.4 Comparison of the 30-D Functions
78(5)
4.4.5 Comparison of the 50-D Functions
83(1)
4.4.6 Discussion
83(3)
4.5 Summary
86(1)
References
87(2)
5 Hybrid Biogeography-Based Optimization Algorithms
89(28)
5.1 Introduction
89(1)
5.2 Hybridization with Differential Evolution
89(15)
5.2.1 The DE/BBO Algorithm
89(2)
5.2.2 Local-DE/BBO
91(6)
5.2.3 Self-adaptive DE/BBO
97(7)
5.3 Hybridization with Harmony Search
104(5)
5.3.1 Biogeographic Harmony Search
104(1)
5.3.2 Computational Experiments
105(4)
5.4 Hybridization with Fireworks Algorithm
109(5)
5.4.1 A Hybrid BBO and FWA Algorithm
109(1)
5.4.2 Computational Experiments
110(4)
5.5 Summary
114(1)
References
114(3)
6 Application of Biogeography-Based Optimization in Transportation
117(26)
6.1 Introduction
117(1)
6.2 BBO for General Transportation Planning
117(6)
6.2.1 A General Transportation Planning Problem
117(2)
6.2.2 BBO Algorithms for the Problem
119(1)
6.2.3 Computational Experiments
119(4)
6.3 BBO for Emergency Transportation Planning
123(4)
6.3.1 An Emergency Transportation Planning Problem
123(1)
6.3.2 A BBO Algorithm for the Problem
124(1)
6.3.3 Computational Experiments
125(2)
6.4 BBO for Emergency Railway Wagon Scheduling
127(10)
6.4.1 An Emergency Railway Wagon Scheduling Problem
128(3)
6.4.2 A Hybrid BBO/DE Algorithm for the Problem
131(3)
6.4.3 Computational Experiments
134(3)
6.5 BBO for Emergency Air Transportation
137(3)
6.5.1 An Emergency Air Transportation Problem
137(2)
6.5.2 BHS and EBO Algorithms for the Problem
139(1)
6.5.3 Computational Experiments
139(1)
6.6 Summary
140(1)
References
141(2)
7 Application of Biogeography-Based Optimization in Job Scheduling
143(34)
7.1 Introduction
143(1)
7.2 BBO for Flow-Shop Scheduling
143(6)
7.2.1 Flow-Shop Scheduling Problem
143(3)
7.2.2 A BBO Algorithm for FSP
146(1)
7.2.3 Computational Experiments
147(2)
7.3 BBO for Job-Shop Scheduling
149(7)
7.3.1 Job-Shop Scheduling Problem
149(2)
7.3.2 An Enhanced BBO Algorithm for the Problem
151(2)
7.3.3 Computational Experiments
153(3)
7.4 BBO for Maintenance Job Assignment and Scheduling
156(7)
7.4.1 A Maintenance Job Assignment and Scheduling Problem
156(2)
7.4.2 A Multi-objective BBO Algorithm for the Problem
158(2)
7.4.3 Computational Experiments
160(3)
7.5 BBO for University Course Timetabling
163(10)
7.5.1 A University Course Timetabling Problem
163(3)
7.5.2 A Discrete EBO Algorithm for UCTP
166(3)
7.5.3 Computational Experiments
169(4)
7.6 Summary
173(1)
References
173(4)
8 Application of Biogeography-Based Optimization in Image Processing
177(22)
8.1 Introduction
177(1)
8.2 BBO for Image Compression
177(4)
8.2.1 Fractal Image Compression
177(3)
8.2.2 BBO Algorithms for Fractal Image Compression
180(1)
8.2.3 Computational Experiments
180(1)
8.3 BBO for Salient Object Detection
181(6)
8.3.1 Salient Object Detection
181(3)
8.3.2 BBO Algorithms for Salient Object Detection
184(1)
8.3.3 Computational Experiments
184(3)
8.4 BBO for Image Segmentation
187(9)
8.4.1 Image Segmentation
187(4)
8.4.2 The Proposed Hybrid BBO-FCM Algorithm
191(1)
8.4.3 Computational Experiments
192(4)
8.5 Summary
196(1)
References
197(2)
9 Biogeography-Based Optimization in Machine Learning
199(20)
9.1 Introduction
199(1)
9.2 BBO for ANN Parameter Optimization
199(4)
9.2.1 The Problem of ANN Parameter Optimization
199(2)
9.2.2 BBO Algorithms for ANN Parameter Optimization
201(1)
9.2.3 Computational Experiment
202(1)
9.3 BBO for ANN Structure and Parameter Optimization
203(4)
9.3.1 The Problem of ANN Structure and Parameter Optimization
203(1)
9.3.2 BBO Algorithms for ANN Structure and Parameter Optimization
204(1)
9.3.3 Computational Experiment
205(2)
9.4 BBO for Fuzzy Neural Network Training
207(5)
9.4.1 The Problem of FNN Training
207(3)
9.4.2 An EBO Algorithm for FNN Parameter Optimization
210(1)
9.4.3 Computational Experiment
211(1)
9.5 BBO for Deep Neural Network Optimization
212(3)
9.5.1 The Problem of DNN Training
212(2)
9.5.2 An EBO Algorithm for DNN Structure and Parameter Optimization
214(1)
9.5.3 Computational Experiment
215(1)
9.6 Summary
215(1)
References
216(3)
Index 219
Prof. Yujun Zheng received his Ph.D. degree from Institute of Software, Chinese Academy of Science in 2010, and now is Professor and Ph.D. supervisor at College of Computer Science and Technology, Zhejiang University of Technology, China. His research interests include intelligent computing and its application in operations research. He has authored over 60 scientific papers in well-known international journals and conferences. In 2014, he received the runner-up of IFORS Prize for OR Development with the work of biogeography-based optimization for emergency engineering rescue scheduling in disaster relief operations in China.

Dr. Xueqin Lu is a Ph.D. candidate at College of Computer Science and Technology, Zhejiang University of Technology, China. Her main research interests are bio-inspired algorithms and their applications.

Dr. Minxia Zhang is an Associate Professor at College of Computer Science and Technology, Zhejiang University of Technology, China. Her main research interests include bio-inspired computation and its applications.

Dr. Shengyong Chen received his Ph.D. degree from City University of Hong Kong in 2003, and now is Professor, Director of Institute of Computer Vision, at College of Computer Science and Technology, Zhejiang University of Technology, China. His research focuses on computer vision and robotics. Prof. Chen is a recipient of National Outstanding Youth Foundation Award (NSFC) in 2014, IEEE Sensors Journal Best Paper Award in 2012, Research Fellowship of Alexander von Humboldt, Germany in 2006 and is also IET fellow, IEEE senior member.