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Evolutionary Computation with Biogeography-Based Optimization.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Somerset :
John Wiley & Sons, Incorporated,
2016.
|
| Colección: | Computer engineering series (London, England). Metaheuristics set ;
v. 8. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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 algorithms3.2.1. 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. Conclusions7. 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.
- 11. Multi-objective BBO11.1. 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.