| Description |
1 online resource |
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text rdacontent |
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computer rdamedia |
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online resource rdacarrier |
| 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 |
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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 |
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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 |
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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 |
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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 |
| 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. 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. |
| Subject |
Combinatorial optimization.
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Optimisation combinatoire.
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Combinatorial optimization
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| Added Author |
Toloo, Mehdi.
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Talatahari, Siamak.
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Rahimi, Iman.
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| Other Form: |
Print version: 0128237996 9780128237991 (OCoLC)1265457617 |
| ISBN |
9780128238004 (electronic bk.) |
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0128238003 (electronic bk.) |
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9780128237991 |
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0128237996 |
| Standard No. |
AU@ 000070759086 |
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UKMGB 020528555 |
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