Evolutionary Computation with Biogeography–based Optimization

Name: Evolutionary Computation with Biogeography–based Optimization
Author: Haiping Ma

Gebonden Engels 2017 9781848218079

193,43

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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 book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.

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ISBN13:9781848218079

Taal:Engels

Bindwijze:gebonden

Aantal pagina's:344

Uitgever:John Wiley & Sons

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Inhoudsopgave

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

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