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Stigmergy as an Approach to Metaheuristic Optimization - Computer ...

Stigmergy as an Approach to Metaheuristic Optimization - Computer ...

VI Table of Contents 3.3

VI Table of Contents 3.3 The multilevel algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 The hybrid algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.5 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.5.1 The experimental environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.5.2 The benchmark suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.5.3 The basic algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.5.4 The multilevel algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.5.5 The hybrid algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4 The multilevel ant-stigmergy approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.1 Problem representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.2 A multilevel paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.1 Coarsening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.2.2 Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2.3 The multilevel algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3 Ant-stigmergy optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.4 The multilevel ant-stigmergy algorithm (MASA) . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.1 Distributed implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.4.2 Grid implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.5 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.5.1 The experimental environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.5.2 The benchmark suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.5.3 Compared algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5.4 The complexity of the algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.5.5 An evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5 The differential ant-stigmergy approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.1 Problem representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.1.1 The fine-grained discrete form of continuous domain . . . . . . . . . . . . . 79 5.1.2 Graph representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2 The differential ant-stigmergy algorithm (DASA) . . . . . . . . . . . . . . . . . . . . . . . 82 5.3 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Table of Contents VII 5.3.1 The experimental environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.3.2 The benchmark suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.3 Compared algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.3.4 The complexity of the algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.3.5 An evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6 Real-world applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.1 Minimizing the power losses of a universal electric motor . . . . . . . . . . . . . . . 93 6.1.1 Definition of the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.1.2 Optimization with the sequential MASA . . . . . . . . . . . . . . . . . . . . . . . . . 97 6.1.3 Optimization with the distributed MASA . . . . . . . . . . . . . . . . . . . . . . . . 103 6.1.4 Optimization with the sequential DASA . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.2 Optimizing the cooling process in continuous steel casting . . . . . . . . . . . . . . 109 6.2.1 Definition of the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 6.2.2 Optimization with the sequential MASA . . . . . . . . . . . . . . . . . . . . . . . . . 112 6.2.3 Optimization with the distributed MASA . . . . . . . . . . . . . . . . . . . . . . . . 112 6.2.4 Optimization with the sequential DASA . . . . . . . . . . . . . . . . . . . . . . . . . 115 7 Conclusion and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 7.1 Contributions to science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 List of Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Stigmergija kot pristop k metahevristični optimizaciji . . . . . . . . . . . . . . . . . . . . . . . 151

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handbook of metaheuristics - Escuela de Ingeniería Informática
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Implementing a Strategic Sourcing Approach to Optimize ... - IIR
Practical approaches to vessel performance optimization
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of evolutionary computation over other approaches - bib tiera ru static
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Continuous and Discrete Optimization Methods in Computer Vision
Integrated Approach to Computer Aided Process Synthesis - CAPEC
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Using Ant Colony Optimization Metaheuristic in Forest ...
A performance comparison of ant stigmergy and differential ...
Metaheuristics for Hard Optimization
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Metaheuristic approaches for optimal broadcasting design in ... - NEO
Application of the Ant Colony Optimization Metaheuristic to ... - CoDE
Metaheuristics in Stochastic Combinatorial Optimization: a ... - Idsia
A Hybrid Metaheuristic Approach to Optimize the Districting Design ...
Optimal Wireless Sensor Network Layout with Metaheuristics ... - NEO
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