1st Edition

Swarm Intelligence Algorithms Modifications and Applications

Edited By Adam Slowik Copyright 2021
    378 Pages 50 B/W Illustrations
    by CRC Press

    378 Pages 50 B/W Illustrations
    by CRC Press

    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, Modi□cations, 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 arti□cial ant selects the edge to travel?
    1.2.3 Pseudo-code of standard ACO algorithm
    1.3 Modi□ed 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
    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 Arti□cial Bee Colony □ Modi□cations and An Application to Software Requirements Selection
    Bahriye Akay
    2.1 Introduction
    2.2 The Original ABC algorithm in brief
    2.3 Modi□cations of the ABC algorithm
    2.3.1 ABC with Modi□ed 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? Objective Function and Constraints Representation Local Search Constraint Handling and Selection Operator
    2.4.3 Description of the Experiments
    2.4.4 Results obtained
    2.5 Conclusions
    3 Modi□ed 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 Modi□cations 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 □ Modi□cations 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 Modi□cations 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 - Modi□cations 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 Modi□cations of the CSO algorithm
    5.3.1 Velocity clamping
    5.3.2 Inertia weight
    5.3.3 Mutation operators
    5.3.4 Acceleration coe□cient 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 Diabetic diet experimental results Mediterranean diet experimental results
    5.5 Conclusions
    6 Chicken Swarm Optimization - Modi□cations 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 Modi□cations 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 classi□cation approaches
    6.5 Conclusions
    7 Cockroach Swarm Optimization □ Modi□cations 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 Modi□cations of the CSO algorithm
    7.3.1 Inertia weight
    7.3.2 Stochastic constriction coe□cient
    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
    8 Crow Search Algorithm - Modi□cations and Application
    Adam Slowik and Dorin Moldovan
    8.1 Introduction
    8.2 Original CSA in brief
    8.3 Modi□cations of CSA
    8.3.1 Chaotic Crow Search Algorithm (CCSA)
    8.3.2 Modi□ed 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
    9 Cuckoo Search Optimisation □ Modi□cations 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 Modi□ed 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: Modi□cations and Application
    Mohammad Majid al-Rifaie, Hooman Oroojeni M. J., and Mihalis Nicolaou
    11.1 Introduction
    11.2 Dispersive Flies Optimisation
    11.3 Modi□cations 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 Model Con□guration DFO Con□guration
    11.4.4 Results
    11.5 Conclusions
    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

    13 Fire□y Algorithm: Variants and Applications
    Xin-She Yang
    13.1 Introduction
    13.2 Fire□y Algorithm
    13.2.1 Standard FA
    13.2.2 Special Cases of FA
    13.3 Variants of Fire□y 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 Lévy 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

    14 Glowworm Swarm Optimization - Modi□cations and Applications
    Krishnanand Kaipa and Debasish Ghose
    14.1 Introduction
    14.2 Brief Description of GSO
    14.3 Modi□cations to GSO Formulation
    14.3.1 Behavior Switching Modi□cation
    14.3.2 Local Optima Mapping Modi□cation
    14.3.3 Coverage Maximization Modi□cation
    14.3.4 Physical Robot Modi□cation
    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 Modi□cation to Clustering
    14.4.3 Application of Coverage Maximization Modi□cation to Wireless Networks
    14.4.4 Application of Physical Robot Modi□cation to Signal Source Localization
    14.5 Conclusions
    15 Grasshopper Optimization Algorithm - Modi□cations and Applications
    Szymon Šukasik
    15.1 Introduction
    15.2 Description of the Original Grasshopper Optimization Algorithm
    15.3 Modi□cations of the GOA technique
    15.3.1 Adaptation to Other Optimization Domains
    15.3.2 Structural Modi□cations
    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

    16 Grey wolf optimizer □ Modi□cations 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 Modi□cations 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 Welded beam design problem Pressure vessel design problem Speed reducer design problem Three-bar truss design problem 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
    17 Hunting Search Optimization Modi□cation 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 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

    18 Krill Herd Algorithm □ Modi□cations 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 Modi□cations 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

    19 Modi□ed Monarch Butter□y Optimization and Real-life Applications

    Pushpendra Singh, Nand K. Meena, and Jin Yang
    19.1 Introduction
    19.2 Monarch butter□y optimization
    19.2.1 Migration Operator
    19.2.2 Butter□y adjusting operator
    19.3 Modi□ed Monarch Butter□y Optimization Method
    19.3.1 Modi□ed migration operator
    19.3.2 Modi□ed butter□y adjustment operator
    19.4 Algorithm of Modi□ed 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 □ Modi□cations 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 Modi□cations of the PSO algorithm
    20.3.1 Velocity clamping
    20.3.2 Inertia weight
    20.3.3 Constriction coe□cient
    20.3.4 Acceleration coe□cients 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
    21 Salp Swarm Algorithm: Modi□cation 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 Modi□cations 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
    22 Social Spider Optimization □ Modi□cations 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 Modi□cations 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

    23 Stochastic Di□usion Search: Modi□cations and Application
    Mohammad Majid al-Rifaie and J. Mark Bishop
    23.1 Introduction
    23.2 SDS algorithm
    23.3 Further modi□cations and adjustments
    23.3.1 Recruitment Strategies Passive Recruitment Mode Active Recruitment Mode Dual Recruitment Mode Context Sensitive Mechanism 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

    24 Whale Optimization Algorithm □ Modi□cations 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 Modi□cations 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


    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).