Multi-Objective Combinatorial Optimization Problems and Solution Methods.

Mehdi. Toloo
Bok · Engelsk · 2022 · Electronic books.

Omfang	1 online resource (316 pages)
Opplysninger	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.. - 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.. - 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.
Sjanger	Electronic books.
Dewey	519.64
ISBN	9780128238004
ISBN(galt)	9780128237991