Evolutionary Computation with Biogeography-based Optimization

Haiping Ma, Shangai University, China. Dan Simon, Professor, Cleveland State University, USA.

• Chapter 1 The Science of Biogeography 11.1 Introduction 11.2 Island biogeography 31.3 Influence factors for biogeography 6Chapter 2 Biogeography and Biological Optimization 112.1 A mathematical model of biogeography 112.2 Biogeography as an optimization process 162.3 Biological optimization 192.3.1 Genetic algorithms 192.3.2 Evolution strategies 202.3.3 Particle swarm optimization 212.3.4 Artificial bee colony algorithm 222.4 Conclusion 23Chapter 3 A Basic BBO Algorithm 253.1 BBO definitions and algorithm 253.1.1 Migration 263.1.2 Mutation 273.1.3 BBO implementation 273.2 Differences between BBO and other optimization algorithms 353.2.1 BBO and genetic algorithms 353.2.2 BBO and other algorithms 363.3 Simulations 373.4 Conclusion 44Chapter 4 BBO Extensions 454.1 Migration curves 454.2 Blended migration 494.3 Other approaches to BBO 514.4 Applications 564.5 Conclusion 59Chapter 5 BBO as a Markov Process 615.1 Markov definitions and notations 615.2 Markov model of BBO 725.3 BBO convergence 795.4 Markov models of BBO extensions 905.5 Conclusions 99Chapter 6 Dynamic System Models of BBO 1036.1 Basic notation 1036.2 Dynamic system models of BBO 1056.3 Applications to benchmark problems 1196.4 Conclusions 122Chapter 7 Statistical Mechanics Approximations of BBO 1237.1 Preliminary foundation 1237.2 Statistical mechanics model of BBO 1287.2.1 Migration 1287.2.2 Mutation 1347.3 Further discussion 1417.3.1 Finite population effects 1417.3.2 Separable fitness functions 1427.4 Conclusions 143Chapter 8 BBO for Combinatorial Optimization 1458.1 Traveling salesman problem 1478.2 BBO for the TSP 1488.2.1 Population initialization 1488.2.2 Migration in the TSP 1508.2.3 Mutation in the TSP 1578.2.4 Implementation framework 1598.3 Graph coloring 1638.4 Knapsack problem 1658.5 Conclusion 167Chapter 9 Constrained BBO 1699.1 Constrained optimization 1709.2 Constraint-handling methods 1729.2.1 Static penalty methods 1729.2.2 Superiority of feasible points 1739.2.3 The eclectic evolutionary algorithm 1749.2.4 Dynamic penalty methods 1749.2.5 Adaptive penalty methods 1769.2.6 The niched-penalty approach 1779.2.7 Stochastic ranking 1789.2.8 ε-level comparisons 1789.3 BBO for constrained optimization 1799.4 Conclusion 185Chapter 10 BBO in Noisy Environments 18710.1 Noisy fitness functions 18810.2 Influence of noise on BBO 19010.3 BBO with re-sampling 19310.4 The Kalman BBO 19610.5 Experimental results 19910.6 Conclusion 201Chapter 11 Multi-objective BBO 20311.1 Multi-objective optimization problems 20411.2 Multi-objective BBO 21111.2.1 Vector evaluated BBO 21111.2.2 Non-dominated sorting BBO 21311.2.3 Niched Pareto BBO 21611.2.4 Strength Pareto BBO 21811.3 Real-world applications 22311.3.1 Warehouse scheduling model 22311.3.2 Optimization of warehouse scheduling 22911.4 Conclusion 231Chapter 12 Hybrid BBO Algorithms 23312.1 Opposition-based BBO 23412.1.1 Opposition definitions and concepts 23412.1.2 Oppositional BBO 23612.1.3 Experimental results 23812.2 BBO with local search 24012.2.1 Local search methods 24012.2.2 Simulation results 24512.3 BBO with other EAs 24712.3.1 Iteration-level hybridization 24712.3.2 Algorithm-level hybridization 25012.3.3 Experimental results 25412.4 Conclusion 256Appendices 259Appendix A Unconstrained Benchmark Functions 261Appendix B Constrained Benchmark Functions 265Appendix C Multi-objective Benchmark Functions 289Bibliography 309Index 325