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Swarm Intelligence Algorithms

Modifications and Applications

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## Book Description

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.

## Table of Contents

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

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 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?

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 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

5.4.4.1 Diabetic diet experimental results

5.4.4.2 Mediterranean diet experimental results

5.5 Conclusions

References

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

References

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

References

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

References

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

11.4.3.1 Model Con□guration

11.4.3.2 DFO Con□guration

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 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

References

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

References

15 Grasshopper Optimization Algorithm - Modi□cations and Applications

Szymon ukasik

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

References

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

16.4.1.1 Welded beam design problem

16.4.1.2 Pressure vessel design problem

16.4.1.3 Speed reducer design problem

16.4.1.4 Three-bar truss design problem

16.4.1.5 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

References

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

17.2.1.1 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

References

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

References

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

References

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

References

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

References

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

23.3.1.1 Passive Recruitment Mode

23.3.1.2 Active Recruitment Mode

23.3.1.3 Dual Recruitment Mode

23.3.1.4 Context Sensitive Mechanism

23.3.1.5 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

References

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

References

## Editor(s)

### Biography

**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).