Swarm Intelligence Algorithms: Modifications and Applications

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
		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
		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
		14.4.2 Application of local optima mapping modification to clustering
		14.4.3 Application of coverage maximization modification to wireless networks
		14.4.4 Application of physical robot modification to signal source localization
	14.5 Conclusions
	References
15. Grasshopper Optimization Algorithm – Modifications and Applications
	15.1 Introduction
	15.2 Description of the original Grasshopper Optimization Algorithm
	15.3 Modifications of the GOA technique
		15.3.1 Adaptation to other optimization domains
		15.3.2 Structural modifications
		15.3.3 Hybrid algorithms
	15.4 Application example: GOA-based clustering
		15.4.1 Clustering and optimization
		15.4.2 Experimental setting and results
	15.5 Conclusion
	References
16. Grey Wolf Optimizer – Modifications and Applications
	16.1 Introduction
	16.2 Original GWO algorithm in brief
		16.2.1 Description of the original GWO algorithm
	16.3 Modifications of the GWO algorithm
		16.3.1 Chaotic maps
		16.3.2 Chaotic grey wolf operator
	16.4 Application of GWO algorithm for engineering optimization problems
		16.4.1 Engineering optimization problems
			16.4.1.1 Welded beam design problem
			16.4.1.2 Pressure vessel design problem
			16.4.1.3 Speed reducer design problem
			16.4.1.4 Three-bar truss design problem
			16.4.1.5 Tension compression spring problem
		16.4.2 Description of experiments
		16.4.3 Convergence curve of CGWO with engineering optimization problems
		16.4.4 Comparison between CGWO and GWO with engineering optimization problems
	16.5 Conclusions
	References
17. Hunting Search Optimization Modification and Application
	17.1 Introduction
	17.2 Original HuS algorithm in brief
		17.2.1 Description of the original hunting search algorithm
	17.3 Improvements in the hunting search algorithm
	17.4 Applications of the algorithm to the welded beam design problem
		17.4.1 Problem description
		17.4.2 How can the hunting search algorithm be used for this problem?
		17.4.3 Description of experiments
		17.4.4 Result obtained
	17.5 Conclusions
	References
18. Krill Herd Algorithm – Modifications and Applications
	18.1 Introduction
	18.2 Original KH algorithm in brief
	18.3 Modifications of the KH algorithm
		18.3.1 Chaotic KH
		18.3.2 Levy-flight KH
		18.3.3 Multi-stage KH
		18.3.4 Stud KH
		18.3.5 KH with linear decreasing step
		18.3.6 Biography-based krill herd
	18.4 Application of KH algorithm for optimum design of retaining walls
		18.4.1 Problem description
		18.4.2 How can KH algorithm be used for this problem?
		18.4.3 Description of experiments
		18.4.4 Results obtained
	18.5 Conclusions
	References
19. Modified Monarch Butterfly Optimization and Real-life Applications
	19.1 Introduction
	19.2 Monarch butterfly optimization
		19.2.1 Migration operator
		19.2.2 Butterfly adjusting operator
	19.3 Modified monarch butterfly optimization method
		19.3.1 Modified migration operator
		19.3.2 Modified butterfly adjustment operator
	19.4 Algorithm of modified MBO
	19.5 Matlab source-code of GCMBO
	19.6 Application of GCMBO for optimal allocation of distributed generations
		19.6.1 Problem statement
		19.6.2 Optimization framework for optimal DG allocation
	19.7 Conclusion
	Acknowledgement
	References
20. Particle Swarm Optimization – Modifications and Application
	20.1 Introduction
	20.2 Original PSO algorithm in brief
		20.2.1 Description of the original PSO algorithm
	20.3 Modifications of the PSO algorithm
		20.3.1 Velocity clamping
		20.3.2 Inertia weight
		20.3.3 Constriction coefficient
		20.3.4 Acceleration coefficients c1 and c2
	20.4 Application of PSO algorithm for IIR digital filter design
		20.4.1 Problem description
		20.4.2 How can the PSO algorithm be used for this problem?
		20.4.3 Description of experiments
		20.4.4 Results obtained
	20.5 Conclusions
	References
21. Salp Swarm Algorithm: Modification and Application
	21.1 Introduction
	21.2 Salp Swarm Algorithm (SSA) in brief
		21.2.1 Inspiration analysis
		21.2.2 Mathematical model for salp chains
	21.3 Modifications of SSA
		21.3.1 Fuzzy logic
		21.3.2 Robust
		21.3.3 Simplex
		21.3.4 Weight factor and adaptive mutation
		21.3.5 Levy flight
		21.3.6 Binary
		21.3.7 Chaotic
		21.3.8 Multi-Objective Problems (MOPS)
	21.4 Application of SSA for welded beam design problem
		21.4.1 Problem description
		21.4.2 How can SSA be used to optimize this problem?
		21.4.3 Result obtained
	21.5 Conclusion
	References
22. Social Spider Optimization – Modifications and Applications
	22.1 Introduction
	22.2 Original SSO algorithm in brief
		22.2.1 Description of the original SSO algorithm
	22.3 Modifications of the SSO algorithm
		22.3.1 Chaotic maps
		22.3.2 Chaotic female cooperative operator
		22.3.3 Chaotic male cooperative operator
	22.4 Application of SSO algorithm for an economic load dispatch problem
		22.4.1 Economic load dispatch problem
		22.4.2 Problem Constraints
		22.4.3 Penalty function
		22.4.4 How can the SSO algorithm be used for an economic load dispatch problem?
		22.4.5 Description of experiments
		22.4.6 Results obtained
	22.5 Conclusions
	References
23. Stochastic Diffusion Search: Modifications and Application
	23.1 Introduction
	23.2 SDS algorithm
	23.3 Further modifications and adjustments
		23.3.1 Recruitment Strategies
			23.3.1.1 Passive recruitment mode
			23.3.1.2 Active recruitment mode
			23.3.1.3 Dual recruitment mode
			23.3.1.4 Context sensitive mechanism
			23.3.1.5 Context free mechanism
		23.3.2 Initialisation and termination
		23.3.3 Partial function evaluation
	23.4 Application: Identifying metastasis in bone scans
		23.4.1 Experiment setup
		23.4.2 Results
		23.4.3 Concluding remarks
	23.5 Conclusion
	References
24. Whale Optimization Algorithm – Modifications and Applications
	24.1 Introduction
	24.2 Original WOA algorithm in brief
	24.3 Modifications of WOA algorithm
		24.3.1 Chaotic WOA
		24.3.2 Levy-flight WOA
		24.3.3 Binary WOA
		24.3.4 Improved WOA
	24.4 Application of WOA algorithm for optimum design of shallow foundation
		24.4.1 Problem description
		24.4.2 How can WOA algorithm be used for this problem?
		24.4.3 Description of experiments
		24.4.4 Results obtained
	24.5 Conclusions
	References
Index