Multi-Objective Combinatorial Optimization Problems and Solution Methods

Multi-Objective Combinatorial Optimization Problems and Solution Methods

1st Edition - February 9, 2022

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  • Editors: Mehdi Toloo, Siamak Talatahari, Iman Rahimi
  • Paperback ISBN: 9780128237991
  • eBook ISBN: 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.

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


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

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, Oman

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.

Iman Rahimi

Iman Rahimi, PhD , earned his BSc (Applied Mathematics) in 2009, MSc (Applied Mathematics – Operations Research) in 2011 and his PhD in the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia, in 2017. He is now working at the Faculty of Engineering and Information Technology, University of Technology Sydney, Australia, as a research scholar. His research interests include machine learning and multiobjective optimization. He also has edited three books entitled "Evolutionary Computation in Scheduling", "Big Data Analytics in Supply Chain Management: Theory and Applications", and "Multi-Objective Combinatorial Optimization Problems and Solution Methods" with Wiley, CRC Press (Taylor & Francis Group), and Elsevier, respectively. He has served as an editor for the following journals: International Journal of Renewable Energy Technology (IJRET) and International Journal of Advanced Heuristic and Meta-Heuristic Algorithms. Besides, he has acted as a reviewer for "International Journal of Production Research (Q1 JRC ranking)" and "Research in Transportation Business & Management (Q3 JCR ranking)". He also received several awards namely, research grants from "University of Tabriz (Iran)", "Iran National Science Foundation", "International Research Scholarship (Australia)", "Faculty of Engineering and Information Technology Scholarship (Australia)", and "Nicolas Baudin Scholarship" from Embassy of France in Australia.

Affiliations and Expertise

University of Technology Sydney, Sydney, Australia.

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