Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (316 pages)

ISBN: 9780128238004 , 0128238003

Inhalt: "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."--

Anmerkung: 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 -- 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 -- 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 -- 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 -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- Acknowledgments -- References -- 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 -- References -- 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 -- References -- 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 -- References -- Index -- Back cover.

Weitere Ausg.: ISBN 9780128237991

Weitere Ausg.: ISBN 0128237996

Sprache: Englisch