Swarm Intelligence Algorithms

1 Ant Colony Optimization, Modications, and Application Pushpendra Singh, Nand K. Meena, and Jin Yang 1.1 Introduction 1.2 Standard Ant System 1.2.1 Brief of Ant Colony Optimization 1.2.2 How articial ant selects the edge to travel? 1.2.3 Pseudo-code of standard ACO algorithm 1.3 Modied 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 ACO can help to solve this optimization problem? 1.4.4 Simulation results 1.5 Conclusion 2 Articial Bee Colony Modications and An Application to Software Requirements Selection Bahriye Akay 2.1 Introduction 2.2 The Original ABC algorithm in brief 2.3 Modications of the ABC algorithm 2.3.1 ABC with Modied 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 Modied Bacterial Forging Optimization and Application Neeraj Kanwar, Nand K. Meena, Jin Yang, and Sonam Parashar 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 Modications 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 4 Bat Algorithm Modications and Application Neeraj Kanwar, Nand K. Meena, and Jin Yang 4.1 Introduction 4.2 Original Bat Algorithm in Brief 4.2.1 Random y 4.2.2 Local random walk 4.3 Modications 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 5 Cat Swarm Optimization - Modications and Application Dorin Moldovan, Adam Slowik, Viorica Chifu, and Ioan Salomie 5.1 Introduction 5.2 Original CSO algorithm in brief 5.2.1 Description of the original CSO algorithm 5.3 Modications of the CSO algorithm 5.3.1 Velocity clamping 5.3.2 Inertia weight 5.3.3 Mutation operators 5.3.4 Acceleration coecient 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 - Modications and Application Dorin Moldovan and Adam Slowik 6.1 Introduction 6.2 Original CSO algorithm in brief 6.2.1 Description of the original CSO algorithm 6.3 Modications 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 classication approaches 6.5 Conclusions References 7 Cockroach Swarm Optimization Modications and Application Joanna Kwiecien 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 Modications of the CSO algorithm 7.3.1 Inertia weight 7.3.2 Stochastic constriction coecient 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 - Modications and Application Adam Slowik and Dorin Moldovan 8.1 Introduction 8.2 Original CSA in brief 8.3 Modications of CSA 8.3.1 Chaotic Crow Search Algorithm (CCSA) 8.3.2 Modied 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 Modications and Application Dhanraj Chitara, Nand K. Meena, and Jin Yang 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 Modied 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 10 Improved Dynamic Virtual Bats Algorithm for Identifying a Suspension System Parameters Ali Osman Topal 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 11 Dispersive Flies Optimisation: Modications and Application Mohammad Majid al-Rifaie, Hooman Oroojeni M. J., and Mihalis Nicolaou 11.1 Introduction 11.2 Dispersive Flies Optimisation 11.3 Modications 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 Conguration 11.4.3.2 DFO Conguration 11.4.4 Results 11.5 Conclusions References 12 Improved Elephant Herding Optimization and Application Nand K. Meena and Jin Yang 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 Firey Algorithm: Variants and Applications Xin-She Yang 13.1 Introduction 13.2 Firey Algorithm 13.2.1 Standard FA 13.2.2 Special Cases of FA 13.3 Variants of Firey 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 Levy 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 - Modications and Applications Krishnanand Kaipa and Debasish Ghose 14.1 Introduction 14.2 Brief Description of GSO 14.3 Modications to GSO Formulation 14.3.1 Behavior Switching Modication 14.3.2 Local Optima Mapping Modication 14.3.3 Coverage Maximization Modication 14.3.4 Physical Robot Modication 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 Modication to Clustering 14.4.3 Application of Coverage Maximization Modication to Wireless Networks 14.4.4 Application of Physical Robot Modication to Signal Source Localization 14.5 Conclusions References 15 Grasshopper Optimization Algorithm - Modications and Applications Szymon ukasik 15.1 Introduction 15.2 Description of the Original Grasshopper Optimization Algorithm 15.3 Modications of the GOA technique 15.3.1 Adaptation to Other Optimization Domains 15.3.2 Structural Modications 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 Modications and Applications Ahmed F. Ali and Mohamed A. Tawhid 16.1 Introduction 16.2 Original GWO algorithm in brief 16.2.1 Description of the original GWO algorithm 16.3 Modications 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 problem 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 Modication and Application Ferhat Erdal, Osman Tunca, and Erkan Dogan 17.1 Introduction 17.2 Original HuS Algorithm in Brief 17.2.1 Description of the original hunting search algorithm 17.2.1.1 Description of the global version of the HuS algorithm 17.3 Improvements in the hunting search algorithm 17.4 Applications of the algorithm to theWelded 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 Modications and Applications Ali R. Kashani, Charles V. Camp, Hamed Tohidi, and Adam Slowik 18.1 Introduction 18.2 Original KH algorithm in brief 18.3 Modications of the KH algorithm 18.3.1 Chaotic KH 18.3.2 Levy-ight 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 Modied Monarch Buttery Optimization and Real-life Applications Pushpendra Singh, Nand K. Meena, and Jin Yang 19.1 Introduction 19.2 Monarch buttery optimization 19.2.1 Migration Operator 19.2.2 Buttery adjusting operator 19.3 Modied Monarch Buttery Optimization Method 19.3.1 Modied migration operator 19.3.2 Modied buttery adjustment operator 19.4 Algorithm of Modied 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 20 Particle Swarm Optimization Modications and Application Adam Slowik 20.1 Introduction 20.2 Original PSO algorithm in brief 20.2.1 Description of the original PSO algorithm 20.3 Modications of the PSO algorithm 20.3.1 Velocity clamping 20.3.2 Inertia weight 20.3.3 Constriction coecient 20.3.4 Acceleration coecients c1 and c2 20.4 Application of PSO algorithm for IIR digital lter 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: Modication and Application Essam H. Houssein, Ibrahim E. Mohamed , and Aboul Ella Hassanien 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 Modications of SSA Algorithm 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 to use SSA to optimize this problem? 21.4.3 Result obtained 21.5 Conclusion References 22 Social Spider Optimization Modications and Applications Ahmed F. Ali and Mohamed A. Tawhid 22.1 Introduction 22.2 Original SSO algorithm in brief 22.2.1 Description of the original SSO algorithm 22.3 Modications 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 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 economic load dispatch problem? 22.4.5 Description of experiments 22.4.6 Results obtained 22.5 Conclusions References 23 Stochastic Diusion Search: Modications and Application Mohammad Majid al-Rifaie and J. Mark Bishop 23.1 Introduction 23.2 SDS algorithm 23.3 Further modications 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 Modications and Applications Ali R. Kashani, Charles V. Camp, Moein Armanfar, and Adam Slowik 24.1 Introduction 24.2 Original WOA algorithm in brief 24.3 Modications of WOA algorithm 24.3.1 Chaotic WOA 24.3.2 Levy-ight 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

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• Engenharia electrónica
• Algoritmos e estruturas de dados
• Tecnologia da informação: aspectos gerais