Multi-objective combinatorial optimization problems and solution methods

"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...

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Bibliographic Details
Other Authors:	Toloo, Mehdi, editor (editor), Talatahari, Siamak, editor, Rahimi, Iman, editor
Format:	eBook
Language:	Inglés
Published:	London, UK : Elsevier [2022]
Subjects:	Combinatorial optimization.
See on Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835438606719

Table of Contents:

Front cover
Half title
Title
Copyright
Dedication
Contents
Contributors
Editors Biography
Preface
Acknowledgments
Chapter 1 Multiobjective combinatorial optimization problems: social, keywords, and journal maps
1.1 Introduction
1.2 Methodology
1.3 Data and basic statistics
1.4 Results and discussion
1.4.1 Mapping the cognitive space
1.4.2 Mapping the social space
1.5 Conclusions and direction for future research
References
Chapter 2 The fundamentals and potential of heuristics and metaheuristics for multiobjective combinatorial optimization problems and solution methods
2.1 Introduction
2.2 Multiobjective combinatorial optimization
2.3 Heuristics concepts
2.4 Metaheuristics concepts
2.5 Heuristics and metaheuristics examples
2.5.1 Tabu search
2.6 Evolutionary algorithms (EA)
2.7 Genetic algorithms (GA)
2.8 Simulated annealing
2.9 Particle swarm optimization (PSO)
2.10 Scatter search (SS)
2.11 Greedy randomized adaptive search procedures (GRASP)
2.12 Ant-colony optimization
2.13 Clustering search
2.14 Hybrid metaheuristics
2.15 Differential evolution (DE)
2.16 Teaching learning-based optimization (TLBO)
2.17 Discussion
2.18 Conclusions
2.19 Future trends
References
Chapter 3 A survey on links between multiple objective decision making and data envelopment analysis
3.1 Introduction
3.2 Preliminary discussion
3.2.1 Multiple objective decision making
3.2.2 Data envelopment analysis
3.3 Application of MODM concepts in the DEA methodology
3.3.1 Classical DEA models
3.3.2 Target setting
3.3.3 Value efficiency
3.3.4 Secondary goal models
3.3.5 Common set of weights
3.3.6 DEA-discriminant analysis
3.3.7 Efficient units and efficient hyperplanes
3.4 Classification of usage of DEA in MODM.
3.4.1 Efficient points
3.5 Discussion and conclusion
References
Chapter 4 Improved crow search algorithm based on arithmetic crossover-a novel metaheuristic technique for solving engineering optimization problems
4.1 Introduction
4.2 Materials and methods
4.2.1 Crow search optimization
4.2.2 Arithmetic crossover based on genetic algorithm
4.2.3 Hybrid CO algorithm
4.3 Results and discussion
4.4 Conclusion
Acknowledgments
References
Chapter 5 MOGROM: Multiobjective Golden Ratio Optimization Algorithm
5.1 Introduction
5.1.1 Definition of multiobjective problems (MOPs)
5.1.2 Literature review
5.1.3 Background and related work
5.2 GROM and MOGROM
5.2.1 MOGROM
5.3 Simulation results, investigation, and analysis
5.3.1 First class
5.3.2 Second class
5.3.3 Third class
5.3.4 Fourth class
5.3.5 Fifth class
5.4 Conclusion
References
Chapter 6 Multiobjective charged system search for optimum location of bank branch
6.1 Introduction
6.2 Multiobjective backgrounds
6.2.1 Dominance and Pareto Front
6.2.2 Performance metrics
6.2.2.2 Coverage of Two Sets (CS)
6.3 Utilized methods
6.3.1 NSGA-II algorithm
6.3.2 MOPSO algorithm
6.3.3 MOCSS algorithm
6.4 Analytic Hierarchy Process
6.5 Model formulation
6.6 Implementation and results
6.7 Conclusions
References
Chapter 7 Application of multiobjective Gray Wolf Optimization in gasification-based problems
7.1 Introduction
7.2 Systems description
7.2.1 Downdraft gasifier
7.2.2 Waste-to-energy plant
7.3 Modeling
7.4 Multicriteria Gray Wolf Optimization
7.5 Results and discussion
7.5.1 Optimization at the gasifier level
7.5.2 Optimization at the WtEP Level
References.
Chapter 8 A VDS-NSGA-II algorithm for multiyear multiobjective dynamic generation and transmission expansion planning
8.1 Introduction
8.2 Problem formulation
8.2.1 Master problem
8.2.2 Slave problem
8.2.3 TC assessment objective of the MMDGTEP problem
8.2.4 EENSHL-II evaluation procedure of the MMDGTEP problem
8.3 Multiobjective optimization principle
8.4 Nondominated sorting genetic algorithm-II
8.4.1 Computational flow of NSGA-II
8.4.2 VDS-NSGA-II
8.4.3 Methodology
8.4.4 VIKOR decision making
8.5 Simulation results
8.6 Conclusion
Acknowledgment
References
Chapter 9 A multiobjective Cuckoo Search Algorithm for community detection in social networks
9.1 Introduction
9.2 Related works
9.3 Proposed model
9.3.1 Community diagnosis
9.3.2 Multiobjective optimization
9.3.3 CD based on MOCSA
9.3.4 Fitness function
9.4 Evaluation and results
9.5 Conclusion and future works
References
Chapter 10 Finding efficient solutions of the multicriteria assignment problem
10.1 Introduction
10.2 The basic AP
10.3 Restated MCAP and DEA: models and relationship
10.3.1 The multicriteria assignment problem (MCAP)
10.3.2 Data envelopment analysis
10.3.3 An integrated DEA and MCAP
10.4 Finding efficient solutions using DEA
10.4.1 The two-phase algorithm
10.4.2 The proposed algorithm
10.5 Numerical examples
10.6 Conclusion
Acknowledgments
References
Chapter 11 Application of multiobjective optimization in thermal design and analysis of complex energy systems
11.1 Introduction
11.1.1 System boundaries
11.1.2 Optimization criteria
11.1.3 Variables
11.1.4 The mathematical model
11.1.5 Suboptimization
11.2 Types of optimization problems
11.2.1 Single-objective optimization
11.2.2 Multiobjective optimization.
11.3 Optimization of energy systems
11.3.1 Thermodynamic optimization and economic optimization
11.3.2 Thermoeconomic optimization
11.4 Literature survey on the optimization of complex energy systems
11.5 Thermodynamic modeling of energy systems
11.5.1 Mass balance
11.5.2 Energy balance
11.5.3 Entropy balance
11.5.4 Exergy balance
11.5.5 Energy efficiency
11.5.6 Exergy efficiency
11.6 Thermoeconomics methodology for optimization of energy systems
11.6.1 The SPECO method
11.6.2 The F (fuel) and P (product) rules
11.7 Sensitivity analysis of energy systems
11.8 Example of application (case study)
11.8.1 Integrated biomass trigeneration system
11.8.2 Results and discussion
11.8.3 Sensitivity analysis
11.9 Conclusions
References
Chapter 12 A multiobjective nonlinear combinatorial model for improved planning of tour visits using a novel binary gaining-sharing knowledge- based optimization algorithm
12.1 Introduction
12.2 Tourism in Egypt: an overview
12.2.1 Tourism in Egypt
12.2.2 Tourism in Cairo
12.2.3 Planning of tour visits
12.3 PTP versus both the TSP and KP
12.3.1 The Traveling Salesman Problem and its variations
12.3.2 Multiobjective 0-1 KP
12.3.3 Basic differences between PTP and both the TSP and KP
12.4 Mathematical model for planning of tour visits
12.5 A real application case study
12.5.1 Ramses Hilton Hotel
12.6 Proposed methodology
12.6.1 Gaining Sharing Knowledge-based optimization algorithm (GSK)
12.6.2 Binary Gaining Sharing Knowledge-based optimization algorithm (BGSK)
12.7 Experimental results
12.8 Conclusions and points for future studies
References
Chapter 13 Variables clustering method to enable planning of large supply chains
13.1 Introduction
13.2 SCP at a glance
13.3 SCP instances as MOCO models.
13.4 Orders clustering for mix-planning
13.5 Variables clustering for the general SCP paradigm
13.6 Conclusions
References
Index
Back cover.

Multi-objective combinatorial optimization problems and solution methods

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