Swarm intelligence algorithms. Modifications and applications / (2020)

• Record Type: Book
• Other Titles: Modifications and applications
• Further Information: Note: Edited by Adam Slowik.
• Editors: Slowik, Adam
• 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 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 &
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2020
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 006.3824; Swarm intelligence; Algorithms; Mathematical optimization
• Languages: English
• ISBNs: 9780429749469; 9780429749476; 9780429749452; 9780429422607
• Related ISBNs: 9781138391017
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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