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E-grāmata: Swarm Intelligence Algorithms: Modifications and Applications

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Edited by (Koszalin University of Technology, Poland.)
  • Formāts: 378 pages
  • Izdošanas datums: 25-Aug-2020
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9780429749469
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Swarm Intelligence Algorithms: Modifications and ApplicationsPalielināt
  • Formāts: 378 pages
  • Izdošanas datums: 25-Aug-2020
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9780429749469
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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.
Preface xix
Editor xxiii
Contributors xxv
1 Ant Colony Optimization, Modifications, and Application
1(14)
Pushpendra Singh
Nand K. Meena
Jin Yang
1.1 Introduction
2(1)
1.2 Standard ant system
2(4)
1.2.1 Brief of ant colony optimization
2(3)
1.2.2 How does the artificial ant select the edge to travel?
5(1)
1.2.3 Pseudo-code of standard ACO algorithm
6(1)
1.3 Modified variants of ant colony optimization
6(4)
1.3.1 Elitist ant systems
6(1)
1.3.2 Ant colony system
7(1)
1.3.3 Max-min ant system
8(1)
1.3.4 Rank based ant systems
9(1)
1.3.5 Continuous orthogonal ant systems
9(1)
1.4 Application of ACO to solve real-life engineering optimization problem
10(3)
1.4.1 Problem description
10(1)
1.4.2 Problem formulation
10(1)
1.4.3 How can ACO help to solve this optimization problem?
11(1)
1.4.4 Simulation results
12(1)
1.5 Conclusion
13(2)
2 Artificial Bee Colony - Modifications and An Application to Software Requirements Selection
15(14)
Bahriye Akay
2.1 Introduction
15(1)
2.2 The Original ABC algorithm in brief
16(2)
2.3 Modifications of the ABC algorithm
18(6)
2.3.1 ABC with modified local search
18(1)
2.3.2 Combinatorial version of ABC
19(2)
2.3.3 Constraint handling ABC
21(1)
2.3.4 Multi-objective ABC
22(2)
2.4 Application of ABC algorithm for software requirement selection
24(3)
2.4.1 Problem description
24(1)
2.4.2 How can the ABC algorithm be used for this problem?
24(1)
2.4.2.1 Objective function and constraints
24(1)
2.4.2.2 Representation
25(1)
2.4.2.3 Local search
25(1)
2.4.2.4 Constraint handling and selection operator
25(1)
2.4.3 Description of the experiments
25(1)
2.4.4 Results obtained
26(1)
2.5 Conclusions
27(1)
References
27(2)
3 Modified Bacterial Foraging Optimization and Application
29(14)
Neeraj Kanwar
Nand K. Meena
Jin Yang
Sonam Parashar
3.1 Introduction
30(1)
3.2 Original BFO algorithm in brief
31(3)
3.2.1 Chemotaxis
31(1)
3.2.2 Swarming
32(1)
3.2.3 Reproduction
32(1)
3.2.4 Elimination and dispersal
33(1)
3.2.5 Pseudo-codes of the original BFO algorithm
33(1)
3.3 Modifications in bacterial foraging optimization
34(2)
3.3.1 Non-uniform elimination-dispersal probability distribution
34(1)
3.3.2 Adaptive chemotaxis step
35(1)
3.3.3 Varying population
36(1)
3.4 Application of BFO for optimal DER allocation in distribution systems
36(4)
3.4.1 Problem description
36(1)
3.4.2 Individual bacteria structure for this problem
37(1)
3.4.3 How can the BFO algorithm be used for this problem?
37(1)
3.4.4 Description of experiments
38(2)
3.4.5 Results obtained
40(1)
3.5 Conclusions
40(3)
4 Bat Algorithm - Modifications and Application
43(14)
Neeraj Kanwar
Nand K. Meena
Jin Yang
4.1 Introduction
44(1)
4.2 Original bat algorithm in brief
45(1)
4.2.1 Random fly
45(1)
4.2.2 Local random walk
45(1)
4.3 Modifications of the bat algorithm
46(4)
4.3.1 Improved bat algorithm
46(1)
4.3.2 Bat algorithm with centroid strategy
47(1)
4.3.3 Self-adaptive bat algorithm (SABA)
47(1)
4.3.4 Chaotic mapping based BA
48(1)
4.3.5 Self-adaptive BA with step-control and mutation mechanisms
48(1)
4.3.6 Adaptive position update
49(1)
4.3.7 Smart bat algorithm
49(1)
4.3.8 Adaptive weighting function and velocity
49(1)
4.4 Application of BA for optimal DNR problem of distribution system
50(3)
4.4.1 Problem description
50(1)
4.4.2 How can the BA algorithm be used for this problem?
50(2)
4.4.3 Description of experiments
52(1)
4.4.4 Results
53(1)
4.5 Conclusion
53(4)
5 Cat Swarm Optimization - Modifications and Application
57(18)
Dorin Moldovan
Adam Slowik
Viorica Chifu
Loan Salomie
5.1 Introduction
58(1)
5.2 Original CSO algorithm in brief
58(3)
5.2.1 Description of the original CSO algorithm
60(1)
5.3 Modifications of the CSO algorithm
61(2)
5.3.1 Velocity clamping
61(1)
5.3.2 Inertia weight
61(1)
5.3.3 Mutation operators
62(1)
5.3.4 Acceleration coefficient c1
62(1)
5.3.5 Adaptation of CSO for diets recommendation
63(1)
5.4 Application of CSO algorithm for recommendation of diets
63(7)
5.4.1 Problem description
63(1)
5.4.2 How can the CSO algorithm be used for this problem?
64(3)
5.4.3 Description of experiments
67(1)
5.4.4 Results obtained
68(1)
5.4.4.1 Diabetic diet experimental results
68(1)
5.4.4.2 Mediterranean diet experimental results
69(1)
5.5 Conclusions
70(1)
References
71(4)
6 Chicken Swarm Optimization - Modifications and Application
75(16)
Dorin Moldovan
Adam Slowik
6.1 Introduction
76(1)
6.2 Original CSO algorithm in brief
76(3)
6.2.1 Description of the original CSO algorithm
77(2)
6.3 Modifications of the CSO algorithm
79(2)
6.3.1 Improved Chicken Swarm Optimization (ICSO)
79(1)
6.3.2 Mutation Chicken Swarm Optimization (MCSO)
79(1)
6.3.3 Quantum Chicken Swarm Optimization (QCSO)
80(1)
6.3.4 Binary Chicken Swarm Optimization (BCSO)
80(1)
6.3.5 Chaotic Chicken Swarm Optimization (CCSO)
80(1)
6.3.6 Improved Chicken Swarm Optimization - Rooster Hen Chick (ICSO-RHC)
81(1)
6.4 Application of CSO for detection of falls in daily living activities
81(6)
6.4.1 Problem description
81(1)
6.4.2 How can the CSO algorithm be used for this problem?
82(1)
6.4.3 Description of experiments
83(1)
6.4.4 Results obtained
84(2)
6.4.5 Comparison with other classification approaches
86(1)
6.5 Conclusions
87(1)
References
88(3)
7 Cockroach Swarm Optimization - Modifications and Application
91(12)
Joanna Kwiecien
7.1 Introduction
91(1)
7.2 Original CSO algorithm in brief
92(3)
7.2.1 Pseudo-code of CSO algorithm
92(1)
7.2.2 Description of the original CSO algorithm
93(2)
7.3 Modifications of the CSO algorithm
95(1)
7.3.1 Inertia weight
95(1)
7.3.2 Stochastic constriction coefficient
95(1)
7.3.3 Hunger component
95(1)
7.3.4 Global and local neighborhoods
96(1)
7.4 Application of CSO algorithm for traveling salesman problem
96(4)
7.4.1 Problem description
96(1)
7.4.2 How can the CSO algorithm be used for this problem?
97(2)
7.4.3 Description of experiments
99(1)
7.4.4 Results obtained
99(1)
7.5 Conclusions
100(1)
References
100(3)
8 Crow Search Algorithm - Modifications and Application
103(16)
Adam Slowik
Dorin Moldovan
8.1 Introduction
103(1)
8.2 Original CSA in brief
104(1)
8.3 Modifications of CSA
105(2)
8.3.1 Chaotic Crow Search Algorithm (CCSA)
105(1)
8.3.2 Modified Crow Search Algorithm (MCSA)
106(1)
8.3.3 Binary Crow Search Algorithm (BCSA)
107(1)
8.4 Application of CSA for jobs status prediction
107(8)
8.4.1 Problem description
107(3)
8.4.2 How can CSA be used for this problem?
110(2)
8.4.3 Experiments description
112(2)
8.4.4 Results
114(1)
8.5 Conclusions
115(1)
References
116(3)
9 Cuckoo Search Optimisation - Modifications and Application
119(14)
Dhanraj Chitara
Nand K. Meena
Jin Yang
9.1 Introduction
120(1)
9.2 Original CSO algorithm in brief
120(3)
9.2.1 Breeding behavior of cuckoo
120(1)
9.2.2 Levy flights
121(1)
9.2.3 Cuckoo search optimization algorithm
121(2)
9.3 Modified CSO algorithms
123(1)
9.3.1 Gradient free cuckoo search
123(1)
9.3.2 Improved cuckoo search for reliability optimization problems
123(1)
9.4 Application of CSO algorithm for designing power system stabilizer
124(5)
9.4.1 Problem description
124(1)
9.4.2 Objective function and problem formulation
124(2)
9.4.3 Case study on two-area four machine power system
126(1)
9.4.4 Eigenvalue analysis of TAFM power system without and with PSSs
126(1)
9.4.5 Time-domain simulation of TAFM power system
127(1)
9.4.6 Performance indices results and discussion of TAFM power system
128(1)
9.5 Conclusion
129(4)
10 Improved Dynamic Virtual Bats Algorithm for Identifying a Suspension System Parameters
133(12)
Ali Osman Topal
10.1 Introduction
133(1)
10.2 Original Dynamic Virtual Bats Algorithm (DVBA)
134(2)
10.3 Improved Dynamic Virtual Bats Algorithm (IDVBA)
136(2)
10.3.1 The weakness of DVBA
136(1)
10.3.2 Improved Dynamic Virtual Bats Algorithm (IDVBA)
136(2)
10.4 Application of IDVBA for identifying a suspension system
138(4)
10.5 Conclusions
142(3)
11 Dispersive Flies Optimisation: Modifications and Application
145(18)
Mohammad Majid al-Rifaie
Hooman Oroojeni M. J.
Mihalis Nicolaou
11.1 Introduction
145(2)
11.2 Dispersive flies optimisation
147(2)
11.3 Modifications in DFO
149(2)
11.3.1 Update equation
149(1)
11.3.2 Disturbance threshold, A
150(1)
11.4 Application: Detecting false alarms in ICU
151(7)
11.4.1 Problem description
152(1)
11.4.2 Using dispersive flies optimisation
153(1)
11.4.3 Experiment setup
154(1)
11.4.3.1 Model configuration
154(1)
11.4.3.2 DFO configuration
155(1)
11.4.4 Results
156(2)
11.5 Conclusions
158(1)
References
158(5)
12 Improved Elephant Herding Optimization and Application
163(12)
Nand K. Meena
Jin Yang
12.1 Introduction
163(1)
12.2 Original elephant herding optimization
164(1)
12.2.1 Clan updating operator
165(1)
12.2.2 Separating operator
165(1)
12.3 Improvements in elephant herding optimization
165(3)
12.3.1 Position of leader elephant
166(1)
12.3.2 Separation of male elephant
166(1)
12.3.3 Chaotic maps
166(1)
12.3.4 Pseudo-code of improved EHO algorithm
167(1)
12.4 Application of IEHO for optimal economic dispatch of microgrids
168(4)
12.4.1 Problem statement
168(2)
12.4.2 Application of EHO to solve this problem
170(1)
12.4.3 Application in Matlab and source-code
170(2)
12.5 Conclusions
172(1)
Acknowledgement
173(1)
References
173(2)
13 Firefly Algorithm: Variants and Applications
175(12)
Xin-She Yang
13.1 Introduction
175(1)
13.2 Firefly algorithm
176(2)
13.2.1 Standard FA
176(1)
13.2.2 Special cases of FA
177(1)
13.3 Variants of firefly algorithm
178(5)
13.3.1 Discrete FA
178(1)
13.3.2 Chaos-based FA
179(1)
13.3.3 Randomly attracted FA with varying steps
180(1)
13.3.4 FA via Levy flights
180(1)
13.3.5 FA with quaternion representation
181(1)
13.3.6 Multi-objective FA
181(1)
13.3.7 Other variants of FA
182(1)
13.4 Applications of FA and its variants
183(1)
13.5 Conclusion
184(1)
References
184(3)
14 Glowworm Swarm Optimization - Modifications and Applications
187(16)
Krishnanand Kaipa
Debasish Ghose
14.1 Introduction
187(1)
14.2 Brief description of GSO
188(1)
14.3 Modifications to GSO formulation
189(5)
14.3.1 Behavior switching modification
189(2)
14.3.2 Local optima mapping modification
191(1)
14.3.3 Coverage maximization modification
192(1)
14.3.4 Physical robot modification
193(1)
14.4 Engineering applications of GSO
194(5)
14.4.1 Application of behavior switching to multiple source localization and boundary mapping
194(2)
14.4.2 Application of local optima mapping modification to clustering
196(1)
14.4.3 Application of coverage maximization modification to wireless networks
196(1)
14.4.4 Application of physical robot modification to signal source localization
197(2)
14.5 Conclusions
199(1)
References
200(3)
15 Grasshopper Optimization Algorithm - Modifications and Applications
203(12)
Szymon Lukasik
15.1 Introduction
203(1)
15.2 Description of the original Grasshopper Optimization Algorithm
204(2)
15.3 Modifications of the GOA technique
206(2)
15.3.1 Adaptation to other optimization domains
206(1)
15.3.2 Structural modifications
207(1)
15.3.3 Hybrid algorithms
207(1)
15.4 Application example: GOA-based clustering
208(3)
15.4.1 Clustering and optimization
208(1)
15.4.2 Experimental setting and results
209(2)
15.5 Conclusion
211(1)
References
212(3)
16 Grey Wolf Optimizer - Modifications and Applications
215(14)
Ahmed F. Ali
Mohamed A. Tawhid
16.1 Introduction
216(1)
16.2 Original GWO algorithm in brief
216(2)
16.2.1 Description of the original GWO algorithm
217(1)
16.3 Modifications of the GWO algorithm
218(1)
16.3.1 Chaotic maps
218(1)
16.3.2 Chaotic grey wolf operator
218(1)
16.4 Application of GWO algorithm for engineering optimization problems
219(6)
16.4.1 Engineering optimization problems
219(1)
16.4.1.1 Welded beam design problem
219(1)
16.4.1.2 Pressure vessel design problem
220(1)
16.4.1.3 Speed reducer design problem
221(1)
16.4.1.4 Three-bar truss design problem
222(1)
16.4.1.5 Tension compression spring problem
223(1)
16.4.2 Description of experiments
223(1)
16.4.3 Convergence curve of CGWO with engineering optimization problems
223(1)
16.4.4 Comparison between CGWO and GWO with engineering optimization problems
224(1)
16.5 Conclusions
225(1)
References
225(4)
17 Hunting Search Optimization Modification and Application
229(12)
Ferhat Erdal
Osman Tunca
Erkan Dogan
17.1 Introduction
229(1)
17.2 Original HuS algorithm in brief
230(4)
17.2.1 Description of the original hunting search algorithm
230(4)
17.3 Improvements in the hunting search algorithm
234(1)
17.4 Applications of the algorithm to the welded beam design problem
234(5)
17.4.1 Problem description
234(1)
17.4.2 How can the hunting search algorithm be used for this problem?
235(2)
17.4.3 Description of experiments
237(1)
17.4.4 Result obtained
237(2)
17.5 Conclusions
239(1)
References
239(2)
18 Krill Herd Algorithm - Modifications and Applications
241(16)
Ali R. Kashani
Charles V. Camp
Earned Tohidi
Adam Slowik
18.1 Introduction
242(1)
18.2 Original KH algorithm in brief
242(2)
18.3 Modifications of the KH algorithm
244(5)
18.3.1 Chaotic KH
244(1)
18.3.2 Levy-flight KH
245(1)
18.3.3 Multi-stage KH
246(1)
18.3.4 StudKH
247(1)
18.3.5 KH with linear decreasing step
247(1)
18.3.6 Biography-based krill herd
248(1)
18.4 Application of KH algorithm for optimum design of retaining walls
249(5)
18.4.1 Problem description
249(1)
18.4.2 How can KH algorithm be used for this problem?
250(2)
18.4.3 Description of experiments
252(1)
18.4.4 Results obtained
252(2)
18.5 Conclusions
254(1)
References
254(3)
19 Modified Monarch Butterfly Optimization and Real-life Applications
257(16)
Pushpendra Singh
Nand K. Meena
Jin Yang
19.1 Introduction
258(1)
19.2 Monarch butterfly optimization
259(1)
19.2.1 Migration operator
259(1)
19.2.2 Butterfly adjusting operator
260(1)
19.3 Modified monarch butterfly optimization method
260(2)
19.3.1 Modified migration operator
261(1)
19.3.2 Modified butterfly adjustment operator
261(1)
19.4 Algorithm of modified MBO
262(2)
19.5 Matlab source-code of GCMBO
264(1)
19.6 Application of GCMBO for optimal allocation of distributed generations
265(4)
19.6.1 Problem statement
265(2)
19.6.2 Optimization framework for optimal DG allocation
267(2)
19.7 Conclusion
269(4)
20 Particle Swarm Optimization - Modifications and Application
273(12)
Adam Slowik
20.1 Introduction
273(1)
20.2 Original PSO algorithm in brief
274(3)
20.2.1 Description of the original PSO algorithm
274(3)
20.3 Modifications of the PSO algorithm
277(2)
20.3.1 Velocity clamping
277(1)
20.3.2 Inertia weight
277(1)
20.3.3 Constriction coefficient
278(1)
20.3.4 Acceleration coefficients c1 and c2
278(1)
20.4 Application of PSO algorithm for IIR digital filter design
279(4)
20.4.1 Problem description
279(1)
20.4.2 How can the PSO algorithm be used for this problem?
280(2)
20.4.3 Description of experiments
282(1)
20.4.4 Results obtained
282(1)
20.5 Conclusions
283(1)
References
283(2)
21 Salp Swarm Algorithm: Modification and Application
285(16)
Essam H. Houssein
Ibrahim E. Mohamed
Aboul Ella Hassanien
21.1 Introduction
286(1)
21.2 Salp Swarm Algorithm (SSA) in brief
287(2)
21.2.1 Inspiration analysis
287(1)
21.2.2 Mathematical model for salp chains
287(2)
21.3 Modifications of SSA
289(3)
21.3.1 Fuzzy logic
289(1)
21.3.2 Robust
290(1)
21.3.3 Simplex
290(1)
21.3.4 Weight factor and adaptive mutation
290(1)
21.3.5 Levy flight
290(1)
21.3.6 Binary
291(1)
21.3.7 Chaotic
291(1)
21.3.8 Multi-Objective Problems (MOPS)
292(1)
21.4 Application of SSA for welded beam design problem
292(3)
21.4.1 Problem description
292(1)
21.4.2 How can SSA be used to optimize this problem?
293(2)
21.4.3 Result obtained
295(1)
21.5 Conclusion
295(1)
References
296(5)
22 Social Spider Optimization - Modifications and Applications
301(12)
Ahmed F. Ali
Mohamed A. Tawhid
22.1 Introduction
301(1)
22.2 Original SSO algorithm in brief
302(3)
22.2.1 Description of the original SSO algorithm
302(3)
22.3 Modifications of the SSO algorithm
305(1)
22.3.1 Chaotic maps
305(1)
22.3.2 Chaotic female cooperative operator
306(1)
22.3.3 Chaotic male cooperative operator
306(1)
22.4 Application of SSO algorithm for an economic load dispatch problem
306(5)
22.4.1 Economic load dispatch problem
306(1)
22.4.2 Problem constraints
307(1)
22.4.3 Penalty function
307(1)
22.4.4 How can the SSO algorithm be used for an economic load dispatch problem?
308(1)
22.4.5 Description of experiments
308(1)
22.4.6 Results obtained
309(2)
22.5 Conclusions
311(1)
References
311(2)
23 Stochastic Diffusion Search: Modifications and Application
313(18)
Mohammad Majid al-Rifaie
J. Mark Bishop
23.1 Introduction
313(1)
23.2 SDS algorithm
314(1)
23.3 Further modifications and adjustments
315(5)
23.3.1 Recruitment strategies
315(1)
23.3.1.1 Passive recruitment mode
315(1)
23.3.1.2 Active recruitment mode
316(1)
23.3.1.3 Dual recruitment mode
316(1)
23.3.1.4 Context sensitive mechanism
317(1)
23.3.1.5 Context free mechanism
318(1)
23.3.2 Initialisation and termination
318(1)
23.3.3 Partial function evaluation
319(1)
23.4 Application: Identifying metastasis in bone scans
320(5)
23.4.1 Experiment setup
321(1)
23.4.2 Results
322(2)
23.4.3 Concluding remarks
324(1)
23.5 Conclusion
325(1)
References
325(6)
24 Whale Optimization Algorithm - Modifications and Applications
331(14)
Ali R. Kashani
Charles V. Camp
Moein Armanfar
Adam Slowik
24.1 Introduction
332(1)
24.2 Original WOA algorithm in brief
332(2)
24.3 Modifications of WOA algorithm
334(4)
24.3.1 Chaotic WOA
334(1)
24.3.2 Levy-flight WOA
334(2)
24.3.3 Binary WOA
336(1)
24.3.4 Improved WOA
337(1)
24.4 Application of WOA algorithm for optimum design of shallow foundation
338(5)
24.4.1 Problem description
338(2)
24.4.2 How can WOA algorithm be used for this problem?
340(1)
24.4.3 Description of experiments
341(1)
24.4.4 Results obtained
342(1)
24.5 Conclusions
343(1)
References
343(2)
Index 345
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).
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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