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E-raamat: Evolutionary Computation with Biogeography-based Optimization [Wiley Online]

(Shaoxing University, China), (Cleveland State University, USA)
  • Formaat: 352 pages
  • Ilmumisaeg: 17-Jan-2017
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119136504
  • ISBN-13: 9781119136507
Teised raamatud teemal:
  • Wiley Online
  • Hind: 174,45 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Evolutionary Computation with Biogeography-based OptimizationSuurem pilt
  • Formaat: 352 pages
  • Ilmumisaeg: 17-Jan-2017
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119136504
  • ISBN-13: 9781119136507
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119136507

Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This manuscript intends to explain the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.

Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This manuscript intends to explain the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.

Chapter 1 The Science of Biogeography
1(10)
1.1 Introduction
1(2)
1.2 Island biogeography
3(3)
1.3 Influence factors for biogeography
6(5)
Chapter 2 Biogeography and Biological Optimization
11(14)
2.1 A mathematical model of biogeography
11(5)
2.2 Biogeography as an optimization process
16(3)
2.3 Biological optimization
19(4)
2.3.1 Genetic algorithms
19(1)
2.3.2 Evolution strategies
20(1)
2.3.3 Particle swarm optimization
21(1)
2.3.4 Artificial bee colony algorithm
22(1)
2.4 Conclusion
23(2)
Chapter 3 A Basic BBO Algorithm
25(20)
3.1 BBO definitions and algorithm
25(10)
3.1.1 Migration
26(1)
3.1.2 Mutation
27(1)
3.1.3 BBO implementation
27(8)
3.2 Differences between BBO and other optimization algorithms
35(2)
3.2.1 BBO and genetic algorithms
35(1)
3.2.2 BBO and other algorithms
36(1)
3.3 Simulations
37(7)
3.4 Conclusion
44(1)
Chapter 4 BBO Extensions
45(16)
4.1 Migration curves
45(4)
4.2 Blended migration
49(2)
4.3 Other approaches to BBO
51(5)
4.4 Applications
56(3)
4.5 Conclusion
59(2)
Chapter 5 BBO as a Markov Process
61(42)
5.1 Markov definitions and notations
61(11)
5.2 Markov model of BBO
72(7)
5.3 BBO convergence
79(11)
5.4 Markov models of BBO extensions
90(9)
5.5 Conclusions
99(4)
Chapter 6 Dynamic System Models of BBO
103(20)
6.1 Basic notation
103(2)
6.2 Dynamic system models of BBO
105(14)
6.3 Applications to benchmark problems
119(3)
6.4 Conclusions
122(1)
Chapter 7 Statistical Mechanics Approximations of BBO
123(22)
7.1 Preliminary foundation
123(5)
7.2 Statistical mechanics model of BBO
128(13)
7.2.1 Migration
128(6)
7.2.2 Mutation
134(7)
7.3 Further discussion
141(2)
7.3.1 Finite population effects
141(1)
7.3.2 Separable fitness functions
142(1)
7.4 Conclusions
143(2)
Chapter 8 BBO for Combinatorial Optimization
145(24)
8.1 Traveling salesman problem
147(1)
8.2 BBO for the TSP
148(15)
8.2.1 Population initialization
148(2)
8.2.2 Migration in the TSP
150(7)
8.2.3 Mutation in the TSP
157(2)
8.2.4 Implementation framework
159(4)
8.3 Graph coloring
163(2)
8.4 Knapsack problem
165(2)
8.5 Conclusion
167(2)
Chapter 9 Constrained BBO
169(18)
9.1 Constrained optimization
170(2)
9.2 Constraint-handling methods
172(7)
9.2.1 Static penalty methods
172(1)
9.2.2 Superiority of feasible points
173(1)
9.2.3 The eclectic evolutionary algorithm
174(1)
9.2.4 Dynamic penalty methods
174(2)
9.2.5 Adaptive penalty methods
176(1)
9.2.6 The niched-penalty approach
177(1)
9.2.7 Stochastic ranking
178(1)
9.2.8 e-level comparisons
178(1)
9.3 BBO for constrained optimization
179(6)
9.4 Conclusion
185(2)
Chapter 10 BBO in Noisy Environments
187(16)
10.1 Noisy fitness functions
188(2)
10.2 Influence of noise on BBO
190(3)
10.3 BBO with re-sampling
193(3)
10.4 The Kalman BBO
196(3)
10.5 Experimental results
199(2)
10.6 Conclusion
201(2)
Chapter 11 Multi-objective BBO
203(30)
11.1 Multi-objective optimization problems
204(7)
11.2 Multi-objective BBO
211(12)
11.2.1 Vector evaluated BBO
211(2)
11.2.2 Non-dominated sorting BBO
213(3)
11.2.3 Niched Pareto BBO
216(2)
11.2.4 Strength Pareto BBO
218(5)
11.3 Real-world applications
223(8)
11.3.1 Warehouse scheduling model
223(6)
11.3.2 Optimization of warehouse scheduling
229(2)
11.4 Conclusion
231(2)
Chapter 12 Hybrid BBO Algorithms
233(26)
12.1 Opposition-based BBO
234(6)
12.1.1 Opposition definitions and concepts
234(2)
12.1.2 Oppositional BBO
236(2)
12.1.3 Experimental results
238(2)
12.2 BBO with local search
240(7)
12.2.1 Local search methods
240(5)
12.2.2 Simulation results
245(2)
12.3 BBO with other EAs
247(9)
12.3.1 Iteration-level hybridization
247(3)
12.3.2 Algorithm-level hybridization
250(4)
12.3.3 Experimental results
254(2)
12.4 Conclusion
256(3)
Appendices
259(50)
Appendix A Unconstrained Benchmark Functions
261(4)
Appendix B Constrained Benchmark Functions
265(24)
Appendix C Multi-objective Benchmark Functions
289(20)
Bibliography 309(16)
Index 325
Haiping Ma, Shangai University, China. Dan Simon, Professor, Cleveland State University, USA.
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