
Multi-Objective Combinatorial Optimization Problems and Solution Methods
Description
Key Features
- 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
Readership
Students, educators, and researchers in the field of Data Science, Computer Science, and mathematics. Research scientists and engineers in the fields of operations research, medical and biological sciences, industrial engineers, plant managers as well as research and development scientists
Table of Contents
- Cover Image
- Title Page
- Copyright
- Dedication
- Table of Contents
- Contributors
- Editors Biography
- Preface
- Acknowledgments
- Chapter 1 Multiobjective combinatorial optimization problems: social, keywords, and journal maps
- Abstract
- 1.1 Introduction
- 1.2 Methodology
- 1.3 Data and basic statistics
- 1.4 Results and discussion
- 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
- Abstract
- 2.1 Introduction
- 2.2 Multiobjective combinatorial optimization
- 2.3 Heuristics concepts
- 2.4 Metaheuristics concepts
- 2.5 Heuristics and metaheuristics examples
- 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
- Abstract
- 3.1 Introduction
- 3.2 Preliminary discussion
- 3.3 Application of MODM concepts in the DEA methodology
- 3.4 Classification of usage of DEA in MODM
- 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
- Abstract
- 4.1 Introduction
- 4.2 Materials and methods
- 4.3 Results and discussion
- 4.4 Conclusion
- Acknowledgments
- References
- Chapter 5 MOGROM: Multiobjective Golden Ratio Optimization Algorithm
- Abstract
- 5.1 Introduction
- 5.2 GROM and MOGROM
- 5.3 Simulation results, investigation, and analysis
- 5.4 Conclusion
- References
- Chapter 6 Multiobjective charged system search for optimum location of bank branch
- Abstract
- 6.1 Introduction
- 6.2 Multiobjective backgrounds
- 6.3 Utilized methods
- 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
- Abstract
- 7.1 Introduction
- 7.2 Systems description
- 7.3 Modeling
- 7.4 Multicriteria Gray Wolf Optimization
- 7.5 Results and discussion
- References
- Chapter 8 A VDS-NSGA-II algorithm for multiyear multiobjective dynamic generation and transmission expansion planning
- Abstract
- 8.1 Introduction
- 8.2 Problem formulation
- 8.3 Multiobjective optimization principle
- 8.4 Nondominated sorting genetic algorithm-II
- 8.5 Simulation results
- 8.6 Conclusion
- Acknowledgment
- References
- Chapter 9 A multiobjective Cuckoo Search Algorithm for community detection in social networks
- Abstract
- 9.1 Introduction
- 9.2 Related works
- 9.3 Proposed model
- 9.4 Evaluation and results
- 9.5 Conclusion and future works
- References
- Chapter 10 Finding efficient solutions of the multicriteria assignment problem
- Abstract
- 10.1 Introduction
- 10.2 The basic AP
- 10.3 Restated MCAP and DEA: models and relationship
- 10.4 Finding efficient solutions using DEA
- 10.5 Numerical examples
- 10.6 Conclusion
- Acknowledgments
- References
- Chapter 11 Application of multiobjective optimization in thermal design and analysis of complex energy systems
- Abstract
- 11.1 Introduction
- 11.2 Types of optimization problems
- 11.3 Optimization of energy systems
- 11.4 Literature survey on the optimization of complex energy systems
- 11.5 Thermodynamic modeling of energy systems
- 11.6 Thermoeconomics methodology for optimization of energy systems
- 11.7 Sensitivity analysis of energy systems
- 11.8 Example of application (case study)
- 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
- Abstract
- 12.1 Introduction
- 12.2 Tourism in Egypt: an overview
- 12.2 Tourism in Cairo
- 12.3 PTP versus both the TSP and KP
- 12.4 Mathematical model for planning of tour visits
- 12.5 A real application case study
- 12.6 Proposed methodology
- 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
- Abstract
- 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
Product details
- No. of pages: 314
- Language: English
- Copyright: © Academic Press 2022
- Published: February 9, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128237991
- eBook ISBN: 9780128238004
About the Editors
Mehdi Toloo
Affiliations and Expertise
Siamak Talatahari
Affiliations and Expertise
Iman Rahimi
Affiliations and Expertise
Ratings and Reviews
There are currently no reviews for "Multi-Objective Combinatorial Optimization Problems and Solution Methods"