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Evolutionary Computation with Biogeography-based Optimization

Book Description

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.

Table of Contents

  1. Cover
  2. Title
  3. Copyright
  4. 1 The Science of Biogeography
    1. 1.1. Introduction
    2. 1.2. Island biogeography
    3. 1.3. Influence factors for biogeography
  5. 2 Biogeography and Biological Optimization
    1. 2.1. A mathematical model of biogeography
    2. 2.2. Biogeography as an optimization process
    3. 2.3. Biological optimization
    4. 2.4. Conclusion
  6. 3 A Basic BBO Algorithm
    1. 3.1. BBO definitions and algorithm
    2. 3.2. Differences between BBO and other optimization algorithms
    3. 3.3. Simulations
    4. 3.4. Conclusion
  7. 4 BBO Extensions
    1. 4.1. Migration curves
    2. 4.2. Blended migration
    3. 4.3. Other approaches to BBO
    4. 4.4. Applications
    5. 4.5. Conclusion
  8. 5 BBO as a Markov Process
    1. 5.1. Markov definitions and notations
    2. 5.2. Markov model of BBO
    3. 5.3. BBO convergence
    4. 5.4. Markov models of BBO extensions
    5. 5.5. Conclusions
  9. 6 Dynamic System Models of BBO
    1. 6.1. Basic notation
    2. 6.2. Dynamic system models of BBO
    3. 6.3. Applications to benchmark problems
    4. 6.4. Conclusions
  10. 7 Statistical Mechanics Approximations of BBO
    1. 7.1. Preliminary foundation
    2. 7.2. Statistical mechanics model of BBO
    3. 7.3. Further discussion
    4. 7.4. Conclusions
  11. 8 BBO for Combinatorial Optimization
    1. 8.1. Traveling salesman problem
    2. 8.2. BBO for the TSP
    3. 8.3. Graph coloring
    4. 8.4. Knapsack problem
    5. 8.5. Conclusion
  12. 9 Constrained BBO
    1. 9.1. Constrained optimization
    2. 9.2. Constraint-handling methods
    3. 9.3. BBO for constrained optimization
    4. 9.4. Conclusion
  13. 10 BBO in Noisy Environments
    1. 10.1. Noisy fitness functions
    2. 10.2. Influence of noise on BBO
    3. 10.3. BBO with re-sampling
    4. 10.4. The Kalman BBO
    5. 10.5. Experimental results
    6. 10.6. Conclusion
  14. 11 Multi-objective BBO
    1. 11.1. Multi-objective optimization problems
    2. 11.2. Multi-objective BBO
    3. 11.3. Real-world applications
    4. 11.4. Conclusion
  15. 12 Hybrid BBO Algorithms
    1. 12.1. Opposition-based BBO
    2. 12.2. BBO with local search
    3. 12.3. BBO with other EAs
    4. 12.4. Conclusion
  16. APPENDICES
    1. Appendix A: Unconstrained Benchmark Functions
    2. Appendix B: Constrained Benchmark Functions
    3. Appendix C: Multi-objective Benchmark Functions
  17. Bibliography
  18. Index
  19. End User License Agreement