Swarm Intelligence Algorithms Modifications and Applications

Nature-based algorithms play an important role among artificial intelligence algorithms. Among them are global optimization algorithms called swarm intelligence algorithms. These algorithms that use the behavior of simple agents and various ways of cooperation between them, are used to solve specific problems that are defined by the so-called objective function. Swarm intelligence algorithms are inspired by the social behavior of various animal species, e.g. ant colonies, bird flocks, bee swarms, schools of fish, etc. The family of these algorithms is very large and additionally includes various types of modifications to enable swarm intelligence algorithms to solve problems dealing with areas other than those for which they were originally developed. 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. This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning these algorithms, along with their modifications and practical applications. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work. If the reader wishes to expand his knowledge beyond the basics of swarm intelligence algorithms presented in this book and is interested in more detailed information, we recommend the book "Swarm Intelligence Algorithms: A Tutorial" (Edited by A. Slowik, CRC Press, 2020). It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work.

1 Ant Colony Optimization, Modications, and Application Pushpendra Singh, Nand K. Meena, and Jin Yang1.1 Introduction 1.2 Standard Ant System 1.2.1 Brief of Ant Colony Optimization1.2.2 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 OptimizationProblem 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 results1.5 Conclusion 2 Articial Bee Colony Modications and An Application to Software Requirements Selection Bahriye Akay2.1 Introduction 2.2 The Original ABC algorithm in brief2.3 Modications of the ABC algorithm 2.3.1 ABC with Modied Local Search2.3.2 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 Selection2.4.1 Problem description 2.4.2 How can the ABC algorithm be used for this problem? 2.4.2.1 Objective Function and Constraints2.4.2.2 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 obtained2.5 Conclusions References 3 Modied Bacterial Forging Optimization and Application Neeraj Kanwar, Nand K. Meena, Jin Yang, and Sonam Parashar3.1 Introduction 3.2 Original BFO algorithm in brief3.2.1 Chemotaxis3.2.2 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 distribution3.3.2 Adaptive chemotaxis step 3.3.3 Varying population 3.4 Application of BFO for Optimal DER Allocation in Distribution Systems3.4.1 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 experiments3.4.5 Results obtained 3.5 Conclusions 4 Bat Algorithm Modications and Application Neeraj Kanwar, Nand K. Meena, and Jin Yang4.1 Introduction 4.2 Original Bat Algorithm in Brief4.2.1 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 BA4.3.5 Self-adaptive BA with step-control and mutation mechanisms4.3.6 Adaptive position update 4.3.7 Smart bat algorithm4.3.8 Adaptive weighting function and velocity 4.4 Application of BA for optimal DNR problem of distribution system 4.4.1 Problem description4.4.2 How can the BA algorithm be used for this problem?4.4.3 Description of experiments 4.4.4 Results4.5 Conclusion5 Cat Swarm Optimization - Modications and Application Dorin Moldovan, Adam Slowik, Viorica Chifu, and Ioan Salomie5.1 Introduction 5.2 Original CSO algorithm in brief 5.2.1 Description of the original CSO algorithm 5.3 Modications of the CSO algorithm5.3.1 Velocity clamping 5.3.2 Inertia weight 5.3.3 Mutation operators 5.3.4 Acceleration coecient 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 experiments 5.4.4 Results obtained5.4.4.1 Diabetic diet experimental results 5.4.4.2 Mediterranean diet experimental results 5.5 ConclusionsReferences 6 Chicken Swarm Optimization - Modications and Application Dorin Moldovan and Adam Slowik6.1 Introduction6.2 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 Activities6.4.1 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 obtained6.4.5 Comparison with other classication approaches6.5 Conclusions References 7 Cockroach Swarm Optimization Modications and ApplicationJoanna Kwiecien7.1 Introduction7.2 Original CSO algorithm in brief7.2.1 Pseudo-code of CSO algorithm7.2.2 Description of the original CSO algorithm7.3 Modications of the CSO algorithm7.3.1 Inertia weight7.3.2 Stochastic constriction coecient7.3.3 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 Conclusions References 8 Crow Search Algorithm - Modications and ApplicationAdam Slowik and Dorin Moldovan8.1 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 Prediction8.4.1 Problem description8.4.2 How can CSA be used for this problem?8.4.3 Experiments description8.4.4 Results8.5 ConclusionsReferences9 Cuckoo Search Optimisation Modications and ApplicationDhanraj Chitara, Nand K. Meena, and Jin Yang9.1 Introduction9.2 Original CSO Algorithm in Brief9.2.1 Breeding behavior of cuckoo 9.2.2 Levy Flights9.2.3 Cuckoo search optimization algorithm 9.3 Modied 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 description 9.4.2 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 PSSs 9.4.5 Time-domain simulation of TAFM power system9.4.6 Performance indices results and discussion of TAFM power system9.5 Conclusion10 Improved Dynamic Virtual Bats Algorithm for Identifying a Suspension System Parameters Ali Osman Topal10.1 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 system10.5 Conclusions 11 Dispersive Flies Optimisation: Modications and ApplicationMohammad Majid al-Rifaie, Hooman Oroojeni M. J., and Mihalis Nicolaou11.1 Introduction11.2 Dispersive Flies Optimisation11.3 Modications in DFO11.3.1 Update Equation11.3.2 Disturbance Threshold, 11.4 Application: Detecting false alarms in ICU11.4.1 Problem Description11.4.2 Using Dispersive Flies Optimisation11.4.3 Experiment Setup11.4.3.1 Model Conguration11.4.3.2 DFO Conguration11.4.4 Results11.5 Conclusions References 12 Improved Elephant Herding Optimization and Application Nand K. Meena and Jin Yang12.1 Introduction12.2 Original Elephant Herding Optimization12.2.1 Clan updating operator 12.2.2 Separating operator 12.3 Improvements in Elephant Herding Optimization 12.3.1 Position of leader elephant12.3.2 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 Microgrids12.4.1 Problem Statement12.4.2 Application of EHO to solve this problem 12.4.3 Application in Matlab and Source-code12.5 Conclusions AcknowledgementReferences 13 Firey Algorithm: Variants and ApplicationsXin-She Yang13.1 Introduction 13.2 Firey Algorithm 13.2.1 Standard FA13.2.2 Special Cases of FA13.3 Variants of Firey Algorithm 13.3.1 Discrete FA13.3.2 Chaos-Based FA 13.3.3 Randomly Attracted FA with Varying Steps13.3.4 FA via Lévy Flights13.3.5

Adam Slowik (IEEE Member 2007; IEEE Senior Member 2012) is an Associate Professor in the Department of Electronics and Computer Science, Koszalin University of Technology. His research interests include soft computing, computational intelligence, and, particularly, bio-inspired optimization algorithms and their engineering applications. He was a recipient of one Best Paper Award (IEEE Conference on Human System Interaction - HSI 2008).