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Swarm intelligence algorithms. Modifications and applications / edited by Adam Slowik.

Contributor(s): Slowik, Adam [editor.]Material type: Text

TextPublisher: Boca Raton, FL : CRC Press, 2020Copyright date: ©2021Edition: First editionDescription: 1 online resource (xxviii, 349 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9780429422607; 0429422601; 9780429749469; 0429749465; 9780429749476; 0429749473; 9780429749452; 0429749457Subject(s): Swarm intelligence | Algorithms | Mathematical optimization | COMPUTERS / Computer Engineering | MATHEMATICS / Arithmetic | TECHNOLOGY / ElectricityDDC classification: 006.3/824 LOC classification: Q337.3 | .S9244 2020ebOnline resources: Taylor & Francis | OCLC metadata license agreement

Contents:

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 ApplicationDorin 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. 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 Conclusions11 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.

Summary: "This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem"-- Provided by publisher.

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"This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem"-- Provided by publisher.

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