Genetic Algorithms and Fuzzy Multiobjective Optimization

Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness.
The intended readers of this book are senior undergraduate students, graduate students, researchers, and practitioners in the fields of operations research, computer science, industrial engineering, management science, systems engineering, and other engineering disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real world research applications are used throughout the book to illustrate the presentation. These applications are drawn from complex problems. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.

Contenu

1. Introduction.- 1.1 Introduction and historical remarks.- 1.2 Organization of the book.- 2. Foundations of Genetic Algorithms.- 2.1 Outline of genetic algorithms.- 2.2 Coding, fitness, and genetic operators.- 3. Genetic Algorithms for 01 Programming.- 3.1 Introduction.- 3.2 Multidimensional 01 knapsack problems.- 3.3 01 programming.- 3.4 Conclusion.- 4. Fuzzy Multiobjective 01 Programming.- 4.1 Introduction.- 4.2 Fuzzy multiobjective 01 programming.- 4.3 Fuzzy multiobjective 01 programming with fuzzy numbers.- 4.4 Conclusion.- 5. Genetic Algorithms for Integer Programming.- 5.1 Introduction.- 5.2 Multidimensional integer knapsack problems.- 5.3 Integer programming.- 5.4 Conclusion.- 6. Fuzzy Multiobjective Integer Programming.- 6.1 Introduction.- 6.2 Fuzzy multiobjective integer programming.- 6.3 Fuzzy multiobjective integer programming with fuzzy numbers.- 6.4 Conclusion.- 7. Genetic Algorithms for Nonlinear Programming.- 7.1 Introduction.- 7.2 Floating-point genetic algorithms.- 7.3 GENOCOP III.- 7.4 Revised GENOCOP III.- 7.5 Conclusion.- 8. Fuzzy Multiobjective Nonlinear Programming.- 8.1 Introduction.- 8.2 Multiobjective nonlinear programming.- 8.3 Multiobjective nonlinear programming problem with fuzzy numbers.- 8.4 Conclusion.- 9. Genetic Algorithms for Job-Shop Scheduling.- 9.1 Introduction.- 9.2 Job-shop scheduling.- 9.3 Genetic algorithms for job-shop scheduling.- 10.Fuzzy Multiobjective Job-Shop Scheduling.- 10.1 Introduction.- 10.2 Job-shop scheduling with fuzzy processing time and fuzzy due date.- 10.3 Multiobjective job-shop scheduling under fuzziness.- 11.Some Applications.- 11.1 Flexible scheduling in a machining center.- 11.2 Operation planning of district heating and cooling plants.- 11.3 Coal purchase planning in electric powerplants.- References.