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E-raamat: Multi-Objective Combinatorial Optimization Problems and Solution Methods

Edited by (University of Technology Sydney, Sydney, Australia.), Edited by (Department of Civil Engineering, University of Tabriz, Tabriz, Iran.
School of Civil and Environment Engineering, University of New South Wales, Sydney, Australia.), Edited by
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  • Ilmumisaeg: 09-Feb-2022
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128238004
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Multi-Objective Combinatorial Optimization Problems and Solution MethodsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 09-Feb-2022
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128238004
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Multi-Objective Combinatorial Optimization Problems and Solution Methods discusses the results of a recent multi-objective combinatorial optimization achievement that considered metaheuristic, mathematical programming, heuristic, hyper heuristic and hybrid approaches. In other words, the book presents various multi-objective combinatorial optimization issues that may benefit from different methods in theory and practice. Combinatorial optimization problems appear in a wide range of applications in operations research, engineering, biological sciences and computer science, hence many optimization approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic and algebraic techniques.

This book covers this important topic as computational optimization has become increasingly popular as design optimization and its applications in engineering and industry have become ever more important due to more stringent design requirements in modern engineering practice.

  • Presents a collection of the most up-to-date research, providing a complete overview of multi-objective combinatorial optimization problems and applications
  • Introduces new approaches to handle different engineering and science problems, providing the field with a collection of related research not already covered in the primary literature
  • Demonstrates the efficiency and power of the various algorithms, problems and solutions, including numerous examples that illustrate concepts and algorithms
Contributors xv
Editors Biography xix
Preface xxi
Acknowledgments xxiii
Chapter 1 Multiobjective combinatorial optimization problems: social, keywords, and journal maps
1(8)
Mehdi Toloo
Siamak Talatahari
Amir H. Gandomi
Iman Rahimi
1.1 Introduction
1(1)
1.2 Methodology
1(1)
1.3 Data and basic statistics
2(1)
1.4 Results and discussion
3(4)
1.4.1 Mapping the cognitive space
3(1)
1.4.2 Mapping the social space
4(3)
1.5 Conclusions and direction for future research
7(2)
References
8(1)
Chapter 2 The fundamentals and potential of heuristics and metaheuristics for multiobjective combinatorial optimization problems and solution methods
9(20)
Ana Carolina Borges Monteiro
Reinaldo Padilha Franga
Rangel Arthur
Yuzo Iano
2.1 Introduction
9(1)
2.2 Multiobjective combinatorial optimization
10(2)
2.3 Heuristics concepts
12(2)
2.4 Metaheuristics concepts
14(2)
2.5 Heuristics and metaheuristics examples
16(1)
2.5.1 Tabu search
16(1)
2.6 Evolutionary algorithms (EA)
16(1)
2.7 Genetic algorithms (GA)
17(1)
2.8 Simulated annealing
18(1)
2.9 Particle swarm optimization (PSO)
18(1)
2.10 Scatter search (SS)
19(1)
2.11 Greedy randomized adaptive search procedures (GRASP)
19(1)
2.12 Ant-colony optimization
19(1)
2.13 Clustering search
20(1)
2.14 Hybrid metaheuristics
20(1)
2.15 Differential evolution (DE)
21(1)
2.16 Teaching learning-based optimization (TLBO)
21(1)
2.17 Discussion
21(2)
2.18 Conclusions
23(1)
2.19 Future trends
24(5)
References
24(5)
Chapter 3 A survey on links between multiple objective decision making and data envelopment analysis
29(42)
Amineh Ghazi
Farhad Hosseinzadeh Lotfi
3.1 Introduction
29(2)
3.2 Preliminary discussion
31(4)
3.2.1 Multiple objective decision making
31(1)
3.2.2 Data envelopment analysis
32(3)
3.3 Application of MODM concepts in the DEA methodology
35(21)
3.3.1 Classical DEA models
35(2)
3.3.2 Target setting
37(4)
3.3.3 Value efficiency
41(2)
3.3.4 Secondary goal models
43(3)
3.3.5 Common set of weights
46(5)
3.3.6 DEA-disciiminant analysis
51(3)
3.3.7 Efficient units and efficient hyperplanes
54(2)
3.4 Classification of usage of DEA in MODM
56(3)
3.4.1 Efficient points
56(3)
3.5 Discussion and conclusion
59(12)
References
60(11)
Chapter 4 Improved crow search algorithm based on arithmetic crossover-a novel metaheuristic technique for solving engineering optimization problems
71(20)
S.N. Kumar
A. Lenin Fred
L. R. Jonisha Miriam
Parasuraman Padmanabhan
Balazs Gulycis
H. Ajay Kumar
Nisha Dayana
4.1 Introduction
71(2)
4.2 Materials and methods
73(2)
4.2.1 Crow search optimization
73(1)
4.2.2 Arithmetic crossover based on genetic algorithm
74(1)
4.2.3 Hybrid CO algorithm
74(1)
4.3 Results and discussion
75(12)
4.4 Conclusion
87(4)
Acknowledgments
88(1)
References
88(3)
Chapter 5 MOGROM: Multiobjective Golden Ratio Optimization Algorithm
91(28)
A.F. Nematollahi
A. Rahiminejad
B. Vahidi
5.1 Introduction
91(3)
5.1.1 Definition of multiobjective problems (MOPs)
92(1)
5.1.2 Literature review
93(1)
5.1.3 Background and related work
93(1)
5.2 GROM and MOGROM
94(3)
5.2.1 MOGROM
95(2)
5.3 Simulation results, investigation, and analysis
97(19)
5.3.1 First class
99(2)
5.3.2 Second class
101(2)
5.3.3 Third class
103(8)
5.3.4 Fourth class
111(2)
5.3.5 Fifth class
113(3)
5.4 Conclusion
116(3)
References
116(3)
Chapter 6 Multiobjective charged system search for optimum location of bank branch
119(14)
Siamak Talatahari
Abolfazl Ranjbar
Mohammad Tolouei
Iman Rahimi
6.1 Introduction
119(1)
6.2 Multiobjective backgrounds
120(2)
6.2.1 Dominance and Pareto Front
120(1)
6.2.2 Performance metrics
121(1)
6.3 Utilized methods
122(2)
6.3.1 NSGA-II algorithm
122(1)
6.3.2 MOPSO algorithm
122(1)
6.3.3 MOCSS algorithm
122(2)
6.4 Analytic Hierarchy Process
124(1)
6.5 Model formulation
125(2)
6.6 Implementation and results
127(4)
6.7 Conclusions
131(2)
References
131(2)
Chapter 7 Application of multiobjective Gray Wolf Optimization in gasification-based problems
133(24)
Babak Talatahari
Siamak Talatahari
Ali Habibollahzade
7.1 Introduction
133(1)
7.2 Systems description
134(2)
7.2.1 Downdraft gasifier
134(1)
7.2.2 Waste-to-energy plant
135(1)
7.3 Modeling
136(2)
7.4 Multicriteria Gray Wolf Optimization
138(5)
7.5 Results and discussion
143(14)
7.5.1 Optimization at the gasifier level
143(7)
7.5.2 Optimization at the WtEP Level
150(4)
References
154(3)
Chapter 8 A VDS-NSGA-II algorithm for multiyear multiobjective dynamic generation and transmission expansion planning
157(20)
Ali Esmaeel Nezhad
Mohammad Sadegh Javadi
Alberto Borghetti
Morteza Taherkhani
Alireza Heidari
Joao P.S. Catalao
8.1 Introduction
157(2)
8.2 Problem formulation
159(4)
8.2.1 Master problem
160(1)
8.2.2 Slave problem
161(1)
8.2.3 TC assessment objective of the MMDGTEP problem
161(1)
8.2.4 EENShl-ii evaluation procedure of the MMDGTEP problem
162(1)
8.3 Multiobjective optimization principle
163(1)
8.4 Nondominated sorting genetic algorithm-II
164(6)
8.4.1 Computational flow of NSGA-II
164(1)
8.4.2 VDS-NSGA-II
165(1)
8.4.3 Methodology
165(4)
8.4.4 VIKOR decision making
169(1)
8.5 Simulation results
170(3)
8.6 Conclusion
173(4)
Acknowledgment
174(1)
References
174(3)
Chapter 9 A multiobjective Cuckoo Search Algorithm for community detection in social networks
177(16)
Shafieh Ghafori
Farhad Soleimanian Gharehchopogh
9.1 Introduction
177(1)
9.2 Related works
178(2)
9.3 Proposed model
180(5)
9.3.1 Community diagnosis
180(1)
9.3.2 Multiobjective optimization
180(1)
9.3.3 CD based on MOCSA
181(3)
9.3.4 Fitness function
184(1)
9.4 Evaluation and results
185(5)
9.5 Conclusion and future works
190(3)
References
190(3)
Chapter 10 Finding efficient solutions of the multicriteria assignment problem
193(18)
Emmanuel Kwasi Mensah
Esmaeil Keshavarz
Mehdi Toloo
10.1 Introduction
193(1)
10.2 The basic AP
194(1)
10.3 Restated MCAP and DEA: models and relationship
195(7)
10.3.1 The multicriteria assignment problem (MCAP)
196(2)
10.3.2 Data envelopment analysis
198(4)
10.3.3 An integrated DEA and MCAP
202(1)
10.4 Finding efficient solutions using DEA
202(4)
10.4.1 The two-phase algorithm
203(2)
10.4.2 The proposed algorithm
205(1)
10.5 Numerical examples
206(3)
10.6 Conclusion
209(2)
Acknowledgments
209(1)
References
209(2)
Chapter 11 Application of multiobjective optimization in thermal design and analysis of complex energy systems
211(26)
A. Baghernejad
E. Aslanzadeh
11.1 Introduction
211(2)
11.1.1 System boundaries
211(1)
11.1.2 Optimization criteria
211(1)
11.1.3 Variables
212(1)
11.1.4 The mathematical model
212(1)
11.1.5 Suboptimization
212(1)
11.2 Types of optimization problems
213(2)
11.2.1 Single-objective optimization
213(1)
11.2.2 Multiobjective optimization
213(2)
11.3 Optimization of energ systems
215(2)
11.3.1 Thermodynamic optimization and economic optimization
215(1)
11.3.2 Thermoeconomic optimization
215(2)
11.4 Literature survey on the optimization of complex energy systems
217(1)
11.5 Thermodynamic modeling of energy systems
217(3)
11.5.1 Mass balance
217(1)
11.5.2 Energy balance
217(1)
11.5.3 Entropy balance
218(1)
11.5.4 Exergy balance
218(1)
11.5.5 Energy efficiency
218(1)
11.5.6 Exergy efficiency
219(1)
11.6 Thermoeconomics methodology for optimization of energy systems
220(2)
11.6.1 The SPECO method
221(1)
11.6.2 The F (fuel) and P (product) rules
222(1)
11.7 Sensitivity analysis of energy systems
222(1)
11.8 Example of application (case study)
222(12)
11.8.1 Integrated biomass trigeneration system
222(4)
11.8.2 Results and discussion
226(6)
11.8.3 Sensitivity analysis
232(2)
11.9 Conclusions
234(3)
References
235(2)
Chapter 12 A multiobjective nonlinear combinatorial model for improved planning of tour visits using a novel binary gaining-sharing knowledge-based optimization algorithm
237(28)
Said Ali Hassan
Prachi Agrawal
Talari Ganesh
Ali Wagdy Mohamed
12.1 Introduction
237(1)
12.2 Tourism in Egypt: an overview
238(2)
12.2.1 Tourism in Egypt
238(1)
12.2.2 Tourism in Cairo
238(1)
12.2.3 Planning of tour visits
239(1)
12.3 PTP versus both the TSP and KP
240(4)
12.3.1 The Traveling Salesman Problem and its variations
240(1)
12.3.2 Multiobjective 0-1 KP
240(3)
12.3.3 Basic differences between PTP and both the TSP and KP
243(1)
12.4 Mathematical model for planning of tour visits
244(3)
12.5 A real application case study
247(3)
12.5.1 Ramses Hilton Hotel
248(2)
12.6 Proposed methodology
250(7)
12.6.1 Gaining Sharing Knowledge-based optimization algorithm (GSK)
251(3)
12.6.2 Binary Gaining Sharing Knowledge-based optimization algorithm (BGSK)
254(3)
12.7 Experimental results
257(2)
12.8 Conclusions and points for future studies
259(6)
References
261(4)
Chapter 13 Variables clustering method to enable planning of large supply chains
265(20)
Emilio Bertolotti
13.1 Introduction
265(1)
13.2 SCP at a glance
265(2)
13.3 SCP instances as MOCO models
267(7)
13.4 Orders clustering for mix-planning
274(6)
13.5 Variables clustering for the general SCP paradigm
280(4)
13.6 Conclusions
284(1)
References 285(2)
Index 287
Dr. Mehdi Toloo is a Full Professor in the Faculty of Economics, Technical University of Ostrava, and Faculty of Business Administration, University of Economics, Prague, Czech Republic. He received his Masters of Science in Applied Mathematics and his Ph.D. in Operations Research. Dr. Toloos areas of interest include Operations Research, Decision Analysis, Performance Evaluation, Multi-Objective Programming, and Mathematical Modelling. He has contributed to numerous international conferences as a chair, keynote speaker, and member of the scientific committee. He is an area editor for the Elsevier journal Computers and Industrial Engineering and an associate editor for RAIRO-Operations Research. His publications include the book Introduction to Scientific Computing: 100 Problems and Solutions in Pascal and papers in top-tier journals such as Applied Mathematics and Computers, Applied Mathematic Modeling, Expert Systems with Applications, and Computers and Mathematics with Applications. Dr. Siamak Talatahari received his Ph.D degree in Structural Engineering from University of Tabriz, Iran. After graduation, he joined the University of Tabriz where he is presently Professor of Structural Engineering. He is the author of more than 100 papers published in international journals, 30 papers presented at international conferences and 8 international book chapters. Dr. Talatahari has been recognized as Distinguished Scientist in the Ministry of Science and Technology and as Distinguished Professor at the University of Tabriz. He also teaches at the Yakin Dogu University, Nicosia, Cyprus. In addition, he is a co-author with our author Xin-She Yang of Swarm Intelligence and Bio-Inspired Computation: Structural Optimization Using Krill Herd Algorithm; Metaheuristics in Water, Geotechnical and Transport Engineering, and Metaheuristic Applications in Structures and Infrastructures, all published by as Insights by Elsevier. Iman Rahimi, PhD, is a distinguished research scholar at the University of Technology Sydney, Australia, specializing in machine learning, optimization, and applied mathematics. He holds dual doctorates in Industrial Engineering and Computer Science, along with a BSc and MSc in Applied Mathematics. Dr. Rahimi has authored and edited several influential books, including titles on evolutionary computation and big data analytics, and has contributed extensively to academic literature as a reviewer for high-ranking journals. His editorial experience spans multiple publications, and he has received numerous international awards and research grants, highlighting his significant contributions to the field. With a robust background in operations research, Dr. Rahimi continues to advance knowledge in multiobjective optimization and its applications in various industries.
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