Swarm Intelligence Algorithms Modifications and Applications
This chapter presents a nature-inspired ant colony optimization (ACO) technique, along with its modified variants. The improved versions of this optimization technique are slightly different and effective than that of its standard version. ACO has inspired from the foraging behavior of ant colony an...
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| Hlavní autor: | |
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| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
United States
CRC Press
2020
Taylor & Francis Group |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 0429749473, 9780429749476, 9781138391017, 9780367528881, 1138391018, 0367528886, 9780367496197, 0367496194 |
| On-line přístup: | Získat plný text |
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- 3.2.5 Pseudo-codes of the original BFO algorithm -- 3.3 Modifications in bacterial foraging optimization -- 3.3.1 Non-uniform elimination-dispersal probability distribution -- 3.3.2 Adaptive chemotaxis step -- 3.3.3 Varying population -- 3.4 Application of BFO for optimal DER allocation in distribution systems -- 3.4.1 Problem description -- 3.4.2 Individual bacteria structure for this problem -- 3.4.3 How can the BFO algorithm be used for this problem? -- 3.4.4 Description of experiments -- 3.4.5 Results obtained -- 3.5 Conclusions -- Acknowledgement -- References -- 4. Bat Algorithm - Modifications and Application -- 4.1 Introduction -- 4.2 Original bat algorithm in brief -- 4.2.1 Random fly -- 4.2.2 Local random walk -- 4.3 Modifications of the bat algorithm -- 4.3.1 Improved bat algorithm -- 4.3.2 Bat algorithm with centroid strategy -- 4.3.3 Self-adaptive bat algorithm (SABA) -- 4.3.4 Chaotic mapping based BA -- 4.3.5 Self-adaptive BA with step-control and mutation mechanisms -- 4.3.6 Adaptive position update -- 4.3.7 Smart bat algorithm -- 4.3.8 Adaptive weighting function and velocity -- 4.4 Application of BA for optimal DNR problem of distribution system -- 4.4.1 Problem description -- 4.4.2 How can the BA algorithm be used for this problem? -- 4.4.3 Description of experiments -- 4.4.4 Results -- 4.5 Conclusion -- Acknowledgement -- References -- 5. Cat Swarm Optimization Modifications and Application -- 5.1 Introduction -- 5.2 Original CSO algorithm in brief -- 5.2.1 Description of the original CSO algorithm -- 5.3 Modifications of the CSO algorithm -- 5.3.1 Velocity clamping -- 5.3.2 Inertia weight -- 5.3.3 Mutation operators -- 5.3.4 Acceleration coefficient c1 -- 5.3.5 Adaptation of CSO for diets recommendation -- 5.4 Application of CSO algorithm for recommendation of diets -- 5.4.1 Problem description
- 5.4.2 How can the CSO algorithm be used for this problem? -- 5.4.3 Description of experiments -- 5.4.4 Results obtained -- 5.4.4.1 Diabetic diet experimental results -- 5.4.4.2 Mediterranean diet experimental results -- 5.5 Conclusions -- References -- 6. Chicken Swarm Optimization Modifications and Application -- 6.1 Introduction -- 6.2 Original CSO algorithm in brief -- 6.2.1 Description of the original CSO algorithm -- 6.3 Modifications of the CSO algorithm -- 6.3.1 Improved Chicken Swarm Optimization (ICSO) -- 6.3.2 Mutation Chicken Swarm Optimization (MCSO) -- 6.3.3 Quantum Chicken Swarm Optimization (QCSO) -- 6.3.4 Binary Chicken Swarm Optimization (BCSO) -- 6.3.5 Chaotic Chicken Swarm Optimization (CCSO) -- 6.3.6 Improved Chicken Swarm Optimization Rooster Hen Chick (ICSO-RHC) -- 6.4 Application of CSO for detection of falls in daily living activities -- 6.4.1 Problem description -- 6.4.2 How can the CSO algorithm be used for this problem? -- 6.4.3 Description of experiments -- 6.4.4 Results obtained -- 6.4.5 Comparison with other classification approaches -- 6.5 Conclusions -- References -- 7. Cockroach Swarm Optimization - Modifications and Application -- 7.1 Introduction -- 7.2 Original CSO algorithm in brief -- 7.2.1 Pseudo-code of CSO algorithm -- 7.2.2 Description of the original CSO algorithm -- 7.3 Modifications of the CSO algorithm -- 7.3.1 Inertia weight -- 7.3.2 Stochastic constriction coefficient -- 7.3.3 Hunger component -- 7.3.4 Global and local neighborhoods -- 7.4 Application of CSO algorithm for traveling salesman problem -- 7.4.1 Problem description -- 7.4.2 How can the CSO algorithm be used for this problem? -- 7.4.3 Description of experiments -- 7.4.4 Results obtained -- 7.5 Conclusions -- References -- 8. Crow Search Algorithm - Modifications and Application -- 8.1 Introduction -- 8.2 Original CSA in brief
- 8.3 Modifications of CSA -- 8.3.1 Chaotic Crow Search Algorithm (CCSA) -- 8.3.2 Modified Crow Search Algorithm (MCSA) -- 8.3.3 Binary Crow Search Algorithm (BCSA) -- 8.4 Application of CSA for jobs status prediction -- 8.4.1 Problem description -- 8.4.2 How can CSA be used for this problem? -- 8.4.3 Experiments description -- 8.4.4 Results -- 8.5 Conclusions -- References -- 9. Cuckoo Search Optimisation - Modifications and Application -- 9.1 Introduction -- 9.2 Original CSO algorithm in brief -- 9.2.1 Breeding behavior of cuckoo -- 9.2.2 Levy flights -- 9.2.3 Cuckoo search optimization algorithm -- 9.3 Modified CSO algorithms -- 9.3.1 Gradient free cuckoo search -- 9.3.2 Improved cuckoo search for reliability optimization problems -- 9.4 Application of CSO algorithm for designing power system stabilizer -- 9.4.1 Problem description -- 9.4.2 Objective function and problem formulation -- 9.4.3 Case study on two-area four machine power system -- 9.4.4 Eigenvalue analysis of TAFM power system without and with PSSs -- 9.4.5 Time-domain simulation of TAFM power system -- 9.4.6 Performance indices results and discussion of TAFM power system -- 9.5 Conclusion -- Acknowledgment -- References -- 10. Improved Dynamic Virtual Bats Algorithm for Identifying a Suspension System Parameters -- 10.1 Introduction -- 10.2 Original Dynamic Virtual Bats Algorithm (DVBA) -- 10.3 Improved Dynamic Virtual Bats Algorithm (IDVBA) -- 10.3.1 The weakness of DVBA -- 10.3.2 Improved Dynamic Virtual Bats Algorithm (IDVBA) -- 10.4 Application of IDVBA for identifying a suspension system -- 10.5 Conclusions -- References -- 11. Dispersive Flies Optimisation: Modifications and Application -- 11.1 Introduction -- 11.2 Dispersive flies optimisation -- 11.3 Modifications in DFO -- 11.3.1 Update equation -- 11.3.2 Disturbance threshold, -- 11.4 Application: Detecting false alarms in ICU
- 14.4.2 Application of local optima mapping modification to clustering
- Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Editor -- Contributors -- 1. Ant Colony Optimization, Modifications, and Application -- 1.1 Introduction -- 1.2 Standard ant system -- 1.2.1 Brief of ant colony optimization -- 1.2.2 How does the artificial ant select the edge to travel? -- 1.2.3 Pseudo-code of standard ACO algorithm -- 1.3 Modified variants of ant colony optimization -- 1.3.1 Elitist ant systems -- 1.3.2 Ant colony system -- 1.3.3 Max-min ant system -- 1.3.4 Rank based ant systems -- 1.3.5 Continuous orthogonal ant systems -- 1.4 Application of ACO to solve real-life engineering optimization problem -- 1.4.1 Problem description -- 1.4.2 Problem formulation -- 1.4.3 How can ACO help to solve this optimization problem? -- 1.4.4 Simulation results -- 1.5 Conclusion -- Acknowledgment -- References -- 2. Artificial Bee Colony - Modifications and An Application to Software Requirements Selection -- 2.1 Introduction -- 2.2 The Original ABC algorithm in brief -- 2.3 Modifications of the ABC algorithm -- 2.3.1 ABC with modified local search -- 2.3.2 Combinatorial version of ABC -- 2.3.3 Constraint handling ABC -- 2.3.4 Multi-objective ABC -- 2.4 Application of ABC algorithm for software requirement selection -- 2.4.1 Problem description -- 2.4.2 How can the ABC algorithm be used for this problem? -- 2.4.2.1 Objective function and constraints -- 2.4.2.2 Representation -- 2.4.2.3 Local search -- 2.4.2.4 Constraint handling and selection operator -- 2.4.3 Description of the experiments -- 2.4.4 Results obtained -- 2.5 Conclusions -- References -- 3. Modified Bacterial Foraging Optimization and Application -- 3.1 Introduction -- 3.2 Original BFO algorithm in brief -- 3.2.1 Chemotaxis -- 3.2.2 Swarming -- 3.2.3 Reproduction -- 3.2.4 Elimination and dispersal
- 11.4.1 Problem description -- 11.4.2 Using dispersive flies optimisation -- 11.4.3 Experiment setup -- 11.4.3.1 Model configuration -- 11.4.3.2 DFO configuration -- 11.4.4 Results -- 11.5 Conclusions -- References -- 12. Improved Elephant Herding Optimization and Application -- 12.1 Introduction -- 12.2 Original elephant herding optimization -- 12.2.1 Clan updating operator -- 12.2.2 Separating operator -- 12.3 Improvements in elephant herding optimization -- 12.3.1 Position of leader elephant -- 12.3.2 Separation of male elephant -- 12.3.3 Chaotic maps -- 12.3.4 Pseudo-code of improved EHO algorithm -- 12.4 Application of IEHO for optimal economic dispatch of microgrids -- 12.4.1 Problem statement -- 12.4.2 Application of EHO to solve this problem -- 12.4.3 Application in Matlab and source-code -- 12.5 Conclusions -- Acknowledgement -- References -- 13. Firefly Algorithm: Variants and Applications -- 13.1 Introduction -- 13.2 Firefly algorithm -- 13.2.1 Standard FA -- 13.2.2 Special cases of FA -- 13.3 Variants of firefly algorithm -- 13.3.1 Discrete FA -- 13.3.2 Chaos-based FA -- 13.3.3 Randomly attracted FA with varying steps -- 13.3.4 FA via Lévy flights -- 13.3.5 FA with quaternion representation -- 13.3.6 Multi-objective FA -- 13.3.7 Other variants of FA -- 13.4 Applications of FA and its variants -- 13.5 Conclusion -- References -- 14. Glowworm Swarm Optimization - Modifications and Applications -- 14.1 Introduction -- 14.2 Brief description of GSO -- 14.3 Modifications to GSO formulation -- 14.3.1 Behavior switching modification -- 14.3.2 Local optima mapping modification -- 14.3.3 Coverage maximization modification -- 14.3.4 Physical robot modification -- 14.4 Engineering applications of GSO -- 14.4.1 Application of behavior switching to multiple source localization and boundary mapping