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End User License Agreement
by Amir H. Gandomi, Ali Emrouznejad, Mo M. Jamshidi, Kalyanmoy Deb, Iman Rahimi
Evolutionary Computation in Scheduling
Cover
List of Contributors
Editors’ Biographies
Preface
Acknowledgments
1 Evolutionary Computation in Scheduling
1.1 Introduction
1.2 Analysis
1.3 Scientometric Analysis
1.4 Conclusion and Direction for Future Research
References
2 Role and Impacts of Ant Colony Optimization in Job Shop Scheduling Problems
2.1 Introduction
2.2 Ant Colony Optimization
2.3 Review of Recent Articles
2.4 Results Analysis
2.5 Conclusion
References
3 Advanced Ant Colony Optimization in Healthcare Scheduling
3.1 History of Ant Colony Optimization
3.2 Introduction to ACO as a Metaheuristic
3.3 Other Advanced Ant Colony Optimization
3.4 Introduction to Multi‐Objective Ant Colony Optimization (MOACO)
3.5 Keywords Analysis for Application of ACO in Scheduling
3.6 Application of Bi‐Objective Ant Colony Optimization in Healthcare Scheduling
References
4 Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence
4.1 Introduction
4.2 Problem Formulation
4.3 SI in the TS Problem
4.4 Dynamic Topology: Binary Heap DPSO (BHDPSO) in Scheduling Problem
4.5 Evaluation Metrics
4.6 Simulation Results
4.7 Real‐Time Application – Smart Traffic System
4.8 Conclusion
References
5 Computationally Efficient Scheduling Schemes for Multiple Antenna Systems Using Evolutionary Algorithms and Swarm Optimization
5.1 Introduction and Problem Statement Formulation
5.2 CUAS Scheme Using GA
5.3 Selection of Optimum Quantization Thresholds for MU‐MIMO Systems Using GA
5.4 Selection of Optimum Quantization Thresholds for MU MIMO‐OFDM Systems Using GA
5.5 Scheduling for MIMO Systems Using PSO
5.6 Conclusion
References
6 An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks' Departure
6.1 Introduction and Literature Review
6.2 Proposed Problem
6.3 Red Deer Algorithm (RDA)
6.4 Computational Results
6.5 Conclusion and Future Works
References
7 Application of Sub‐Population Scheduling Algorithm in Multi‐Population Evolutionary Dynamic Optimization
7.1 Introduction
7.2 Literature Review
7.3 Problem Statement
7.4 Theory of Learning Automata
7.5 Scheduling Algorithms
7.6 Application of the SPS Algorithms on a Multi‐Population Evolutionary Dynamic Optimization Method
7.7 Computational Experiments
7.8 Conclusions
Acknowledgments
References
8 Task Scheduling in Cloud Environments
8.1 Introduction
8.2 Physical Topology of Cloud
8.3 Research Methodology
8.4 Results
8.5 Research Gaps and Future Directions
8.6 Conclusion
References
9 Scheduling of Robotic Disassembly in Remanufacturing Using Bees Algorithms
9.1 Introduction
9.2 Related Works
9.3 Disassembly Model
9.4 Optimization Objective
9.5 Bees Algorithm
9.6 Experiments and Results
9.7 Conclusion
Acknowledgments
A. Appendix A
References
10 A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies
10.1 Introduction
10.2 Modeling
10.3 Fireworks Algorithm
10.4 Simulation Results
10.5 Conclusion
Acknowledgment
References
Index
End User License Agreement
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WILEY END USER LICENSE AGREEMENT
WILEY END USER LICENSE AGREEMENT
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