Evolutionary computation with biogeography-based optimization
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 provid...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
London, UK
ISTE : Wiley
2017
John Wiley & Sons, Incorporated Wiley-ISTE Wiley-Blackwell |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781848218079 1848218079 |
| DOI | 10.1002/9781119136507 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Simon, Dan Ma, Haiping |
| Author_xml | – sequence: 1 fullname: Ma, Haiping – sequence: 2 fullname: Simon, Dan |
| BackLink | https://cir.nii.ac.jp/crid/1130000794519790848$$DView record in CiNii |
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| Notes | Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeographybased 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 crossdisciplinary 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 Includes bibliographical references (pages 309-323) and index |
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| PageCount | 349 |
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| PublicationDate | 2017 2017-02-06T00:00:00 2017-01-18 |
| PublicationDateYYYYMMDD | 2017-01-01 2017-02-06 2017-01-18 |
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| PublicationPlace | London, UK |
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| Publisher | ISTE : Wiley John Wiley & Sons, Incorporated Wiley-ISTE Wiley-Blackwell |
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| Snippet | Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization... |
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| SubjectTerms | Evolutionary computation Evolutionary computation. fast (OCoLC)fst00917338 |
| TableOfContents | Cover -- Title Page -- Copyright -- Contents -- 1. The Science of Biogeography -- 1.1. Introduction -- 1.2. Island biogeography -- 1.3. Influence factors for biogeography -- 2. Biogeography and Biological Optimization -- 2.1. A mathematical model of biogeography -- 2.2. Biogeography as an optimization process -- 2.3. Biological optimization -- 2.3.1. Genetic algorithms -- 2.3.2. Evolution strategies -- 2.3.3. Particle swarm optimization -- 2.3.4. Artificial bee colony algorithm -- 2.4. Conclusion -- 3. A Basic BBO Algorithm -- 3.1. BBO definitions and algorithm -- 3.1.1. Migration -- 3.1.2. Mutation -- 3.1.3. BBO implementation -- 3.2. Differences between BBO and other optimization algorithms -- 3.2.1. BBO and genetic algorithms -- 3.2.2. BBO and other algorithms -- 3.3. Simulations -- 3.4. Conclusion -- 4. BBO Extensions -- 4.1. Migration curves -- 4.2. Blended migration -- 4.3. Other approaches to BBO -- 4.4. Applications -- 4.5. Conclusion -- 5. BBO as a Markov Process -- 5.1. Markov definitions and notations -- 5.2. Markov model of BBO -- 5.3. BBO convergence -- 5.4. Markov models of BBO extensions -- 5.5. Conclusions -- 6. Dynamic System Models of BBO -- 6.1. Basic notation -- 6.2. Dynamic system models of BBO -- 6.3. Applications to benchmark problems -- 6.4. Conclusions -- 7. Statistical Mechanics Approximations of BBO -- 7.1. Preliminary foundation -- 7.2. Statistical mechanics model of BBO -- 7.2.1. Migration -- 7.2.2. Mutation -- 7.3. Further discussion -- 7.3.1. Finite population effects -- 7.3.2. Separable fitness functions -- 7.4. Conclusions -- 8. BBO for Combinatorial Optimization -- 8.1. Traveling salesman problem -- 8.2. BBO for the TSP -- 8.2.1. Population initialization -- 8.2.2. Migration in the TSP -- 8.2.3. Mutation in the TSP -- 8.2.4. Implementation framework -- 8.3. Graph coloring -- 8.4. Knapsack problem 8.5. Conclusion -- 9. Constrained BBO -- 9.1. Constrained optimization -- 9.2. Constraint-handling methods -- 9.2.1. Static penalty methods -- 9.2.2. Superiority of feasible points -- 9.2.3. The eclectic evolutionary algorithm -- 9.2.4. Dynamic penalty methods -- 9.2.5. Adaptive penalty methods -- 9.2.6. The niched-penalty approach -- 9.2.7. Stochastic ranking -- 9.2.8. ε-level comparisons -- 9.3. BBO for constrained optimization -- 9.4. Conclusion -- 10. BBO in Noisy Environments -- 10.1. Noisy fitness functions -- 10.2. Influence of noise on BBO -- 10.3. BBO with re-sampling -- 10.4. The Kalman BBO -- 10.5. Experimental results -- 10.6. Conclusion -- 11. Multi-objective BBO -- 11.1. Multi-objective optimization problems -- 11.2. Multi-objective BBO -- 11.2.1. Vector evaluated BBO -- 11.2.2. Non-dominated sorting BBO -- 11.2.3. Niched Pareto BBO -- 11.2.4. Strength Pareto BBO -- 11.3. Real-world applications -- 11.3.1. Warehouse scheduling model -- 11.3.2. Optimization of warehouse scheduling -- 11.4. Conclusion -- 12. Hybrid BBO Algorithms -- 12.1. Opposition-based BBO -- 12.1.1. Opposition definitions and concepts -- 12.1.2. Oppositional BBO -- 12.1.3. Experimental results -- 12.2. BBO with local search -- 12.2.1. Local search methods -- 12.2.2. Simulation results -- 12.3. BBO with other EAs -- 12.3.1. Iteration-level hybridization -- 12.3.2. Algorithm-level hybridization -- 12.3.3. Experimental results -- 12.4. Conclusion -- APPENDICES -- Appendix A: Unconstrained Benchmark Functions -- Appendix B: Constrained Benchmark Functions -- Appendix C: Multi-objective Benchmark Functions -- Bibliography -- Index -- Other titles from iSTE in Computer Engineering -- EULA |
| Title | Evolutionary computation with biogeography-based optimization |
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