Swarm intelligence algorithms. Modifications and applications / edited by Adam Slowik.
Material type:
TextPublisher: Boca Raton, FL : CRC Press, 2020Copyright date: ©2021Edition: First editionDescription: 1 online resource (xxviii, 349 pages) : illustrations (some color)Content type: - text
- computer
- online resource
- 9780429422607
- 0429422601
- 9780429749469
- 0429749465
- 9780429749476
- 0429749473
- 9780429749452
- 0429749457
- 006.3/824 23
- Q337.3 .S9244 2020eb
"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.
<P>1 Ant Colony Optimization, Modications, and Application <BR>Pushpendra Singh, Nand K. Meena, and Jin Yang<BR>1.1 Introduction <BR>1.2 Standard Ant System <BR>1.2.1 Brief of Ant Colony Optimization<BR>1.2.2 How articial ant selects the edge to travel? <BR>1.2.3 Pseudo-code of standard ACO algorithm <BR>1.3 Modied Variants of Ant Colony Optimization <BR>1.3.1 Elitist ant systems <BR>1.3.2 Ant colony system <BR>1.3.3 Max-min ant system <BR>1.3.4 Rank based ant systems <BR>1.3.5 Continuous orthogonal ant systems <BR>1.4 Application of ACO to Solve Real-life Engineering Optimization<BR>Problem <BR>1.4.1 Problem description <BR>1.4.2 Problem formulation <BR>1.4.3 How ACO can help to solve this optimization problem?<BR>1.4.4 Simulation results<BR>1.5 Conclusion <BR>2 Articial Bee Colony Modications and An Application to Software Requirements Selection <BR>Bahriye Akay<BR>2.1 Introduction <BR>2.2 The Original ABC algorithm in brief<BR>2.3 Modications of the ABC algorithm <BR>2.3.1 ABC with Modied Local Search<BR>2.3.2 Combinatorial version of ABC <BR>2.3.3 Constraint Handling ABC <BR>2.3.4 Multi-objective ABC <BR>2.4 Application of ABC algorithm for Software Requirement Selection<BR>2.4.1 Problem description <BR>2.4.2 How can the ABC algorithm be used for this problem? <BR>2.4.2.1 Objective Function and Constraints<BR>2.4.2.2 Representation <BR>2.4.2.3 Local Search <BR>2.4.2.4 Constraint Handling and Selection Operator <BR>2.4.3 Description of the Experiments <BR>2.4.4 Results obtained<BR>2.5 Conclusions <BR>References <BR>3 Modied Bacterial Forging Optimization and Application <BR>Neeraj Kanwar, Nand K. Meena, Jin Yang, and Sonam Parashar<BR>3.1 Introduction <BR>3.2 Original BFO algorithm in brief<BR>3.2.1 Chemotaxis<BR>3.2.2 Swarming <BR>3.2.3 Reproduction <BR>3.2.4 Elimination and dispersal <BR>3.2.5 Pseudo-codes of the original BFO algorithm <BR>3.3 Modications in Bacterial Foraging Optimization <BR>3.3.1 Non-uniform elimination-dispersal probability distribution<BR>3.3.2 Adaptive chemotaxis step <BR>3.3.3 Varying population <BR>3.4 Application of BFO for Optimal DER Allocation in Distribution Systems<BR>3.4.1 Problem description <BR>3.4.2 Individual bacteria structure for this problem <BR>3.4.3 How can the BFO algorithm be used for this problem? <BR>3.4.4 Description of experiments<BR>3.4.5 Results obtained <BR>3.5 Conclusions <BR>4 Bat Algorithm Modications and Application <BR>Neeraj Kanwar, Nand K. Meena, and Jin Yang<BR>4.1 Introduction <BR>4.2 Original Bat Algorithm in Brief<BR>4.2.1 Random y <BR>4.2.2 Local random walk <BR>4.3 Modications of the Bat algorithm <BR>4.3.1 Improved bat algorithm <BR>4.3.2 Bat algorithm with centroid strategy <BR>4.3.3 Self-adaptive bat algorithm (SABA) <BR>4.3.4 Chaotic mapping based BA<BR>4.3.5 Self-adaptive BA with step-control and mutation mechanisms<BR>4.3.6 Adaptive position update <BR>4.3.7 Smart bat algorithm<BR>4.3.8 Adaptive weighting function and velocity <BR>4.4 Application of BA for optimal DNR problem of distribution system <BR>4.4.1 Problem description<BR>4.4.2 How can the BA algorithm be used for this problem?<BR>4.4.3 Description of experiments <BR>4.4.4 Results<BR>4.5 Conclusion<BR>5 Cat Swarm Optimization -- Modications and Application <BR>Dorin Moldovan, Adam Slowik, Viorica Chifu, and Ioan Salomie<BR>5.1 Introduction <BR>5.2 Original CSO algorithm in brief <BR>5.2.1 Description of the original CSO algorithm <BR>5.3 Modications of the CSO algorithm<BR>5.3.1 Velocity clamping <BR>5.3.2 Inertia weight <BR>5.3.3 Mutation operators <BR>5.3.4 Acceleration coecient c1<BR>5.3.5 Adaptation of CSO for diets recommendation<BR>5.4 Application of CSO algorithm for recommendation of diets <BR>5.4.1 Problem description<BR>5.4.2 How can the CSO algorithm be used for this problem? <BR>5.4.3 Description of experiments <BR>5.4.4 Results obtained<BR>5.4.4.1 Diabetic diet experimental results <BR>5.4.4.2 Mediterranean diet experimental results <BR>5.5 Conclusions<BR>References <BR>6 Chicken Swarm Optimization -- Modications and Application</P><P>Dorin Moldovan and Adam Slowik<BR>6.1 Introduction<BR>6.2 Original CSO algorithm in brief <BR>6.2.1 Description of the original CSO algorithm <BR>6.3 Modications of the CSO algorithm <BR>6.3.1 Improved Chicken Swarm Optimization (ICSO) <BR>6.3.2 Mutation Chicken Swarm Optimization (MCSO) </P><P>6.3.3 Quantum Chicken Swarm Optimization (QCSO) <BR>6.3.4 Binary Chicken Swarm Optimization (BCSO)<BR>6.3.5 Chaotic Chicken Swarm Optimization (CCSO) <BR>6.3.6 Improved Chicken Swarm Optimization -- Rooster Hen Chick (ICSO-RHC) <BR>6.4 Application of CSO for Detection of Falls in Daily Living Activities<BR>6.4.1 Problem description <BR>6.4.2 How can the CSO algorithm be used for this problem? <BR>6.4.3 Description of experiments <BR>6.4.4 Results obtained<BR>6.4.5 Comparison with other classication approaches<BR>6.5 Conclusions <BR>References <BR>7 Cockroach Swarm Optimization Modications and Application<BR>Joanna Kwiecien<BR>7.1 Introduction<BR>7.2 Original CSO algorithm in brief<BR>7.2.1 Pseudo-code of CSO algorithm<BR>7.2.2 Description of the original CSO algorithm<BR>7.3 Modications of the CSO algorithm<BR>7.3.1 Inertia weight<BR>7.3.2 Stochastic constriction coecient<BR>7.3.3 Hunger component<BR>7.3.4 Global and local neighborhoods<BR>7.4 Application of CSO algorithm for traveling salesman problem<BR>7.4.1 Problem description<BR>7.4.2 How can the CSO algorithm be used for this problem? <BR>7.4.3 Description of experiments<BR>7.4.4 Results obtained<BR>7.5 Conclusions <BR>References <BR>8 Crow Search Algorithm -- Modications and Application<BR>Adam Slowik and Dorin Moldovan<BR>8.1 Introduction <BR>8.2 Original CSA in brief <BR>8.3 Modications of CSA <BR>8.3.1 Chaotic Crow Search Algorithm (CCSA)<BR>8.3.2 Modied Crow Search Algorithm (MCSA) <BR>8.3.3 Binary Crow Search Algorithm (BCSA) <BR>8.4 Application of CSA for Jobs Status Prediction<BR>8.4.1 Problem description<BR>8.4.2 How can CSA be used for this problem?<BR>8.4.3 Experiments description<BR>8.4.4 Results<BR>8.5 Conclusions<BR>References<BR>9 Cuckoo Search Optimisation Modications and Application<BR>Dhanraj Chitara, Nand K. and Jin Yang<BR>9.1 Introduction<BR>9.2 Original CSO Algorithm in Brief<BR>9.2.1 Breeding behavior of cuckoo <BR>9.2.2 Levy Flights<BR>9.2.3 Cuckoo search optimization algorithm <BR>9.3 Modied CSO Algorithms<BR>9.3.1 Gradient free cuckoo search<BR>9.3.2 Improved cuckoo search for reliability optimization problems<BR>9.4 Application of CSO Algorithm for Designing Power System Stabilizer<BR>9.4.1 Problem description <BR>9.4.2 Objective function and problem formulation<BR>9.4.3 Case study on two-area four machine power system<BR>9.4.4 Eigenvalue analysis of TAFM power system without and with PSSs <BR>9.4.5 Time-domain simulation of TAFM power system<BR>9.4.6 Performance indices results and discussion of TAFM power system<BR>9.5 Conclusion<BR>10 Improved Dynamic Virtual Bats Algorithm for Identifying a Suspension System Parameters <BR>Ali Osman Topal<BR>10.1 Introduction <BR>10.2 Original Dynamic Virtual Bats Algorithm (DVBA) <BR>10.3 Improved Dynamic Virtual Bats Algorithm (IDVBA) <BR>10.3.1 The weakness of DVBA <BR>10.3.2 Improved Dynamic Virtual Bats Algorithm (IDVBA)<BR>10.4 Application of IDVBA for identifying a suspension system<BR>10.5 Conclusions</P><P>11 Dispersive Flies Optimisation: Modications and Application<BR>Mohammad Majid al-Rifaie, Hooman Oroojeni M. J., and Mihalis Nicolaou<BR>11.1 Introduction<BR>11.2 Dispersive Flies Optimisation<BR>11.3 Modications in DFO<BR>11.3.1 Update Equation<BR>11.3.2 Disturbance Threshold, <BR>11.4 Application: Detecting false alarms in ICU<BR>11.4.1 Problem Description<BR>11.4.2 Using Dispersive Flies Optimisation<BR>11.4.3 Experiment Setup<BR>11.4.3.1 Model Conguration<BR>11.4.3.2 DFO Conguration<BR>11.4.4 Results<BR>11.5 Conclusions <BR>References <BR>12 Improved Elephant Herding Optimization and Application <BR>Nand K.
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