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Preface; Contents; Contributors; Members of Review Board; The Nature of Nature: Why Nature-Inspired Algorithms Work; 1 Introduction: How Nature Works; 2 The Nature of Nature; 2.1 Fitness Landscape; 2.2 Graphs and Phase Changes; 3 Nature-Inspired Algorithms; 3.1 Genetic Algorithm; 3.2 Ant Colony Optimization; 3.3 Simulated Annealing; 3.4 Convergence; 4 Dual-Phase Evolution; 4.1 Theory; 4.2 GA; 4.3 Ant Colony Optimization; 4.4 Simulated Annealing; 5 Evolutionary Dynamics; 5.1 Markov Chain Models; 5.2 The Replicator Equation; 6 Generalized Local Search Machines; 6.1 The Model; 6.2 SA; 6.3 GA
6.4 ACO6.5 Discussion; 7 Conclusion; References; Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments; 1 Introduction; 2 Improved Bat Algorithm; 3 IBA for Multimodal Problems; 3.1 Parameter Settings; 3.2 Test Functions; 3.3 Numerical Results; 4 Performance Comparison of IBA with Other Algorithms; 5 IBA Performance in AWGN; 5.1 Numerical Results; 6 Conclusions; References; Multi-objective Ant Colony Optimisation in Wireless Sensor Networks; 1 Introduction; 2 Multi-objective Combinatorial Optimisation Problems
2.1 Combinatorial Optimisation Problems2.2 Multi-objective Combinatorial Optimisation Problems; 2.3 Pareto Optimality; 2.4 Decision-Making; 2.5 Solving Combinatorial Optimisation Problems; 3 Multi-objective Ant Colony Optimisation; 3.1 Origins; 3.2 Multi-objective Ant Colony Optimisation; 4 Applications of MOACO Algorithms in WSNs; 5 Conclusion; References; Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence; 1 Introduction; 2 Artificial Sports Trainer; 3 Generating the Training Plans; 3.1 Preprocessing; 3.2 Optimization Process; 4 Experiments
5 Conclusion with Future IdeasReferences; Limiting Distribution and Mixing Time for Genetic Algorithms; 1 Introduction; 2 Preliminaries; 2.1 Random Search and Markov Chains; 2.2 Boltzmann Distribution and Simulated Annealing; 3 Expected Hitting Time as a Means of Comparison; 3.1 ``No Free Lunch'' Considerations; 4 The Holland Genetic Algorithm; 5 A Simple Genetic Algorithm; 6 Shuffle-Bit GA; 6.1 Results; 6.2 Estimate of Expected Hitting Time; 7 Discussion and Future Work; References; Permutation Problems, Genetic Algorithms, and Dynamic Representations; 1 Introduction; 2 Problem Descriptions
2.1 Bin Packing Problem2.2 Graph Colouring Problem; 2.3 Travelling Salesman Problem; 3 Previous Work on Small Travelling Salesman Problem Instances; 4 Algorithms; 4.1 2-Opt; 4.2 Lin
Kernighan; 4.3 Genetic Algorithm Variations; 4.4 Representation; 5 Experimental Design; 5.1 Bin Packing Problem; 5.2 Graph Colouring Problem; 5.3 Travelling Salesman Problem; 6 Results and Discussion; 6.1 Bin Packing Problem; 6.2 Graph Colouring Problem; 6.3 Travelling Salesman Problem; 7 Conclusions; References
Hybridization of the Flower Pollination Algorithm
A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults
6.4 ACO6.5 Discussion; 7 Conclusion; References; Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments; 1 Introduction; 2 Improved Bat Algorithm; 3 IBA for Multimodal Problems; 3.1 Parameter Settings; 3.2 Test Functions; 3.3 Numerical Results; 4 Performance Comparison of IBA with Other Algorithms; 5 IBA Performance in AWGN; 5.1 Numerical Results; 6 Conclusions; References; Multi-objective Ant Colony Optimisation in Wireless Sensor Networks; 1 Introduction; 2 Multi-objective Combinatorial Optimisation Problems
2.1 Combinatorial Optimisation Problems2.2 Multi-objective Combinatorial Optimisation Problems; 2.3 Pareto Optimality; 2.4 Decision-Making; 2.5 Solving Combinatorial Optimisation Problems; 3 Multi-objective Ant Colony Optimisation; 3.1 Origins; 3.2 Multi-objective Ant Colony Optimisation; 4 Applications of MOACO Algorithms in WSNs; 5 Conclusion; References; Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence; 1 Introduction; 2 Artificial Sports Trainer; 3 Generating the Training Plans; 3.1 Preprocessing; 3.2 Optimization Process; 4 Experiments
5 Conclusion with Future IdeasReferences; Limiting Distribution and Mixing Time for Genetic Algorithms; 1 Introduction; 2 Preliminaries; 2.1 Random Search and Markov Chains; 2.2 Boltzmann Distribution and Simulated Annealing; 3 Expected Hitting Time as a Means of Comparison; 3.1 ``No Free Lunch'' Considerations; 4 The Holland Genetic Algorithm; 5 A Simple Genetic Algorithm; 6 Shuffle-Bit GA; 6.1 Results; 6.2 Estimate of Expected Hitting Time; 7 Discussion and Future Work; References; Permutation Problems, Genetic Algorithms, and Dynamic Representations; 1 Introduction; 2 Problem Descriptions
2.1 Bin Packing Problem2.2 Graph Colouring Problem; 2.3 Travelling Salesman Problem; 3 Previous Work on Small Travelling Salesman Problem Instances; 4 Algorithms; 4.1 2-Opt; 4.2 Lin
Kernighan; 4.3 Genetic Algorithm Variations; 4.4 Representation; 5 Experimental Design; 5.1 Bin Packing Problem; 5.2 Graph Colouring Problem; 5.3 Travelling Salesman Problem; 6 Results and Discussion; 6.1 Bin Packing Problem; 6.2 Graph Colouring Problem; 6.3 Travelling Salesman Problem; 7 Conclusions; References
Hybridization of the Flower Pollination Algorithm
A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults