book

Swarm Intelligence Algorithms (Two Volume Set)

by Adam Slowik

January 2021

Intermediate to advanced

768 pages

9h 17m

English

CRC Press

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1.1 Introduction1.2 Standard ant system1.2.1 Brief of ant colony optimization1.2.2 How does the artificial ant select the edge to travel?.1.2.3 Pseudo-code of standard ACO algorithm1.3 Modified variants of ant colony optimization1.3.1 Elitist ant systems1.3.2 Ant colony system1.3.3 Max-min ant system1.3.4 Rank based ant systems1.3.5 Continuous orthogonal ant systems1.4 Application of ACO to solve real-life engineering optimization problem1.4.1 Problem description1.4.2 Problem formulation1.4.3 How can ACO help to solve this optimization problem?1.4.4 Simulation results1.5 Conclusion

2.1 Introduction2.2 The Original ABC algorithm in brief2.3 Modifications of the ABC algorithm2.3.1 ABC with modified local search2.3.2 Combinatorial version of ABC2.3.3 Constraint handling ABC2.3.4 Multi-objective ABC2.4 Application of ABC algorithm for software requirement selection2.4.1 Problem description2.4.2 How can the ABC algorithm be used for this problem?2.4.2.1 Objective function and constraints2.4.2.2 Representation2.4.2.3 Local search2.4.2.4 Constraint handling and selection operator.2.4.3 Description of the experiments2.4.4 Results obtained2.5 ConclusionsReferences
3.1 Introduction3.2 Original BFO algorithm in brief3.2.1 Chemotaxis3.2.2 Swarming3.2.3 Reproduction3.2.4 Elimination and dispersal3.2.5 Pseudo-codes of the original BFO algorithm3.3 Modifications in bacterial foraging optimization3.3.1 Non-uniform elimination-dispersal probability distribution3.3.2 Adaptive chemotaxis step3.3.3 Varying population3.4 Application of BFO for optimal DER allocation in distribution systems3.4.1 Problem description3.4.2 Individual bacteria structure for this problem3.4.3 How can the BFO algorithm be used for this problem?3.4.4 Description of experiments3.4.5 Results obtained3.5 Conclusions
4.1 Introduction4.2 Original bat algorithm in brief4.2.1 Random fly4.2.2 Local random walk4.3 Modifications of the bat algorithm4.3.1 Improved bat algorithm4.3.2 Bat algorithm with centroid strategy4.3.3 Self-adaptive bat algorithm (SABA)4.3.4 Chaotic mapping based BA4.3.5 Self-adaptive BA with step-control and mutation mechanisms4.3.6 Adaptive position update4.3.7 Smart bat algorithm4.3.8 Adaptive weighting function and velocity4.4 Application of BA for optimal DNR problem of distribution system4.4.1 Problem description4.4.2 How can the BA algorithm be used for this problem?4.4.3 Description of experiments4.4.4 Results4.5 Conclusion
5.1 Introduction5.2 Original CSO algorithm in brief5.2.1 Description of the original CSO algorithm5.3 Modifications of the CSO algorithm5.3.1 Velocity clamping5.3.2 Inertia weight5.3.3 Mutation operators5.3.4 Acceleration coefficient c15.3.5 Adaptation of CSO for diets recommendation5.4 Application of CSO algorithm for recommendation of diets.5.4.1 Problem description5.4.2 How can the CSO algorithm be used for this problem?5.4.3 Description of experiments5.4.4 Results obtained5.4.4.1 Diabetic diet experimental results5.4.4.2 Mediterranean diet experimental results5.5 ConclusionsReferences
6.1 Introduction6.2 Original CSO algorithm in brief6.2.1 Description of the original CSO algorithm6.3 Modifications of the CSO algorithm6.3.1 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 activities6.4.1 Problem description6.4.2 How can the CSO algorithm be used for this problem?6.4.3 Description of experiments6.4.4 Results obtained6.4.5 Comparison with other classification approaches6.5 ConclusionsReferences
7.1 Introduction7.2 Original CSO algorithm in brief7.2.1 Pseudo-code of CSO algorithm7.2.2 Description of the original CSO algorithm7.3 Modifications of the CSO algorithm7.3.1 Inertia weight7.3.2 Stochastic constriction coefficient7.3.3 Hunger component7.3.4 Global and local neighborhoods7.4 Application of CSO algorithm for traveling salesman problem7.4.1 Problem description7.4.2 How can the CSO algorithm be used for this problem?7.4.3 Description of experiments7.4.4 Results obtained7.5 ConclusionsReferences
8.1 Introduction8.2 Original CSA in brief8.3 Modifications of CSA8.3.1 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 prediction8.4.1 Problem description8.4.2 How can CSA be used for this problem?8.4.3 Experiments description8.4.4 Results8.5 ConclusionsReferences
9.1 Introduction9.2 Original CSO algorithm in brief9.2.1 Breeding behavior of cuckoo9.2.2 Levy flights9.2.3 Cuckoo search optimization algorithm9.3 Modified CSO algorithms9.3.1 Gradient free cuckoo search9.3.2 Improved cuckoo search for reliability optimization problems9.4 Application of CSO algorithm for designing power system stabilizer9.4.1 Problem description9.4.2 Objective function and problem formulation9.4.3 Case study on two-area four machine power system9.4.4 Eigenvalue analysis of TAFM power system without and with PSSs9.4.5 Time-domain simulation of TAFM power system9.4.6 Performance indices results and discussion of TAFM power system9.5 Conclusion
10.1 Introduction10.2 Original Dynamic Virtual Bats Algorithm (DVBA)10.3 Improved Dynamic Virtual Bats Algorithm (IDVBA)10.3.1 The weakness of DVBA10.3.2 Improved Dynamic Virtual Bats Algorithm (IDVBA)10.4 Application of IDVBA for identifying a suspension system10.5 Conclusions
11.1 Introduction11.2 Dispersive flies optimisation11.3 Modifications in DFO11.3.1 Update equation11.3.2 Disturbance threshold, Δ11.4 Application: Detecting false alarms in ICU11.4.1 Problem description11.4.2 Using dispersive flies optimisation11.4.3 Experiment setup11.4.3.1 Model configuration11.4.3.2 DFO configuration11.4.4 Results11.5 ConclusionsReferences
12.1 Introduction12.2 Original elephant herding optimization12.2.1 Clan updating operator12.2.2 Separating operator12.3 Improvements in elephant herding optimization12.3.1 Position of leader elephant12.3.2 Separation of male elephant12.3.3 Chaotic maps12.3.4 Pseudo-code of improved EHO algorithm12.4 Application of IEHO for optimal economic dispatch of microgrids12.4.1 Problem statement12.4.2 Application of EHO to solve this problem12.4.3 Application in Matlab and source-code12.5 ConclusionsAcknowledgementReferences
13.1 Introduction13.2 Firefly algorithm13.2.1 Standard FA13.2.2 Special cases of FA13.3 Variants of firefly algorithm13.3.1 Discrete FA13.3.2 Chaos-based FA13.3.3 Randomly attracted FA with varying steps13.3.4 FA via Lévy flights13.3.5 FA with quaternion representation13.3.6 Multi-objective FA13.3.7 Other variants of FA13.4 Applications of FA and its variants13.5 ConclusionReferences
14.1 Introduction14.2 Brief description of GSO14.3 Modifications to GSO formulation14.3.1 Behavior switching modification14.3.2 Local optima mapping modification14.3.3 Coverage maximization modification14.3.4 Physical robot modification14.4 Engineering applications of GSO14.4.1 Application of behavior switching to multiple source localization and boundary mapping14.4.2 Application of local optima mapping modification to clustering14.4.3 Application of coverage maximization modification to wireless networks14.4.4 Application of physical robot modification to signal source localization14.5 ConclusionsReferences
15.1 Introduction15.2 Description of the original Grasshopper Optimization Algorithm15.3 Modifications of the GOA technique15.3.1 Adaptation to other optimization domains15.3.2 Structural modifications15.3.3 Hybrid algorithms15.4 Application example: GOA-based clustering15.4.1 Clustering and optimization15.4.2 Experimental setting and results15.5 ConclusionReferences
16.1 Introduction16.2 Original GWO algorithm in brief16.2.1 Description of the original GWO algorithm16.3 Modifications of the GWO algorithm16.3.1 Chaotic maps16.3.2 Chaotic grey wolf operator16.4 Application of GWO algorithm for engineering optimization problems16.4.1 Engineering optimization problems16.4.1.1 Welded beam design problem16.4.1.2 Pressure vessel design problem16.4.1.3 Speed reducer design problem16.4.1.4 Three-bar truss design problem16.4.1.5 Tension compression spring problem16.4.2 Description of experiments16.4.3 Convergence curve of CGWO with engineering optimization problems16.4.4 Comparison between CGWO and GWO with engineering optimization problems16.5 ConclusionsReferences
17.1 Introduction17.2 Original HuS algorithm in brief17.2.1 Description of the original hunting search algorithm.17.3 Improvements in the hunting search algorithm17.4 Applications of the algorithm to the welded beam design problem17.4.1 Problem description17.4.2 How can the hunting search algorithm be used for this problem?17.4.3 Description of experiments17.4.4 Result obtained17.5 ConclusionsReferences
18.1 Introduction18.2 Original KH algorithm in brief18.3 Modifications of the KH algorithm18.3.1 Chaotic KH18.3.2 Levy-flight KH18.3.3 Multi-stage KH18.3.4 Stud KH18.3.5 KH with linear decreasing step18.3.6 Biography-based krill herd18.4 Application of KH algorithm for optimum design of retaining walls18.4.1 Problem description18.4.2 How can KH algorithm be used for this problem?18.4.3 Description of experiments18.4.4 Results obtained18.5 ConclusionsReferences
19.1 Introduction19.2 Monarch butterfly optimization19.2.1 Migration operator19.2.2 Butterfly adjusting operator19.3 Modified monarch butterfly optimization method19.3.1 Modified migration operator19.3.2 Modified butterfly adjustment operator19.4 Algorithm of modified MBO19.5 Matlab source-code of GCMBO19.6 Application of GCMBO for optimal allocation of distributed generations19.6.1 Problem statement19.6.2 Optimization framework for optimal DG allocation19.7 Conclusion
20.1 Introduction20.2 Original PSO algorithm in brief20.2.1 Description of the original PSO algorithm20.3 Modifications of the PSO algorithm20.3.1 Velocity clamping20.3.2 Inertia weight20.3.3 Constriction coefficient20.3.4 Acceleration coefficients c1 and c220.4 Application of PSO algorithm for IIR digital filter design20.4.1 Problem description20.4.2 How can the PSO algorithm be used for this problem?20.4.3 Description of experiments20.4.4 Results obtained20.5 ConclusionsReferences
21.1 Introduction21.2 Salp Swarm Algorithm (SSA) in brief21.2.1 Inspiration analysis21.2.2 Mathematical model for salp chains21.3 Modifications of SSA21.3.1 Fuzzy logic21.3.2 Robust21.3.3 Simplex21.3.4 Weight factor and adaptive mutation21.3.5 Levy flight21.3.6 Binary21.3.7 Chaotic21.3.8 Multi-Objective Problems (MOPS)21.4 Application of SSA for welded beam design problem21.4.1 Problem description21.4.2 How can SSA be used to optimize this problem?21.4.3 Result obtained21.5 ConclusionReferences
22.1 Introduction22.2 Original SSO algorithm in brief22.2.1 Description of the original SSO algorithm22.3 Modifications of the SSO algorithm22.3.1 Chaotic maps22.3.2 Chaotic female cooperative operator22.3.3 Chaotic male cooperative operator22.4 Application of SSO algorithm for an economic load dispatch problem22.4.1 Economic load dispatch problem22.4.2 Problem constraints22.4.3 Penalty function22.4.4 How can the SSO algorithm be used for an economic load dispatch problem?22.4.5 Description of experiments22.4.6 Results obtained22.5 ConclusionsReferences
23.1 Introduction23.2 SDS algorithm23.3 Further modifications and adjustments23.3.1 Recruitment Strategies23.3.1.1 Passive recruitment mode23.3.1.2 Active recruitment mode23.3.1.3 Dual recruitment mode23.3.1.4 Context sensitive mechanism23.3.1.5 Context free mechanism23.3.2 Initialisation and termination23.3.3 Partial function evaluation23.4 Application: Identifying metastasis in bone scans23.4.1 Experiment setup23.4.2 Results23.4.3 Concluding remarks23.5 ConclusionReferences
24.1 Introduction24.2 Original WOA algorithm in brief24.3 Modifications of WOA algorithm24.3.1 Chaotic WOA24.3.2 Levy-flight WOA24.3.3 Binary WOA24.3.4 Improved WOA24.4 Application of WOA algorithm for optimum design of shallow foundation24.4.1 Problem description24.4.2 How can WOA algorithm be used for this problem?.24.4.3 Description of experiments24.4.4 Results obtained24.5 ConclusionsReferences

Overview

This set of two books can provides the basics for understanding how swarm intelligence algorithms work, together with their modifications and practical applications. It is useful for students studying the basics of nature-based optimization algorithms, and can be a helpful for learning to solve a selected practical problem.