Multi-objective combinatorial optimization problems and solution methods / edited by Mehdi Toloo, Siamak Talatahari and Iman Rahimi.

Format:

Book

Contributor:

Toloo, Mehdi, editor.

Talatahari, Siamak, editor.

Rahimi, Iman, editor.

Language:

English

Subjects (All):

Combinatorial optimization.

Physical Description:

1 online resource (316 pages)

Place of Publication:

London, UK : Elsevier, [2022]

Summary:

"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."-- Title details screen.

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

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

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

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

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

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

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

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

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

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

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

Index

Back cover.

Notes:

Includes bibliographical references and index.

Description based on print version record and other sources.

ISBN:

9780128238004

0128238003

OCLC:

1296408831