Multi-Objective Combinatorial Optimization Problems and Solution Methods
- 1st Edition - February 9, 2022
- Imprint: Academic Press
- Editors: Mehdi Toloo, Siamak Talatahari, Iman Rahimi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 7 9 9 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 8 0 0 - 4
Multi-Objective Combinatorial Optimization Problems and Solution Methods discusses the results of a recent multi-objective combinatorial optimization achievement that considere… Read more
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Request a sales quoteMulti-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
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
- 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
- Edition: 1
- Published: February 9, 2022
- Imprint: Academic Press
- No. of pages: 314
- Language: English
- Paperback ISBN: 9780128237991
- eBook ISBN: 9780128238004
MT
Mehdi Toloo
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. Toloo’s 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.
Affiliations and expertise
Department of Business Transformation, Surrey Business School, University of Surrey, Guildford GU2 7XH, United Kingdom.
Department of Systems Engineering, Faculty of Economics, Technical University of Ostrava, Ostrava, Czech Republic
Department of Operations Management & Business Statistics, College of Economics and Political Science, Sultan Qaboos University, Muscat, OmanST
Siamak Talatahari
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.
Affiliations and expertise
Department of Civil Engineering, University of Tabriz, Tabriz, Iran.
School of Civil and Environment Engineering, University of New South Wales, Sydney, Australia.IR
Iman Rahimi
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.
Affiliations and expertise
University of Technology Sydney, Sydney, Australia.Read Multi-Objective Combinatorial Optimization Problems and Solution Methods on ScienceDirect