Genetic algorithms and fuzzy multiobjective optimization

Masatoshi Sakawa0792374525, 9780792374527

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.

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Table of contents :
Front cover……Page 1
Title page……Page 3
Date-line……Page 4
Dedication……Page 5
Contents……Page 7
Preface……Page 9
1.1 Introduction and historical remarks……Page 11
1.2 Organization of the book……Page 17
2.1 Outline of genetic algorithms……Page 21
2.2 Coding, fitness, and genetic operators……Page 25
3.1 Introduction……Page 39
3.2 Multidimensional 0-1 knapsack problems……Page 40
3.3 0-1 programming……Page 49
3.4 Conclusion……Page 62
4.1 Introduction……Page 63
4.2 Fuzzy multiobjective 0-1 programming……Page 64
4.3 Fuzzy multiobjective 0-1 programming with fuzzy numbers……Page 80
4.4 Conclusion……Page 91
5.1 Introduction……Page 93
5.2 Multidimensional integer knapsack problems……Page 94
5.3 Integer programming……Page 108
5.4 Conclusion……Page 114
6.1 Introduction……Page 117
6.2 Fuzzy multiobjective integer programming……Page 118
6.3 Fuzzy multiobjective integer programming with fuzzy numbers……Page 128
6.4 Conclusion……Page 141
7.1 Introduction……Page 143
7.2 Floating-point genetic algorithms……Page 144
7.3 GENOCOP III……Page 151
7.4 Revised GENOCOP III……Page 153
7.5 Conclusion……Page 161
8.1 Introduction……Page 163
8.2 Multiobjective nonlinear programming……Page 164
8.3 Multiobjective nonlinear programming problem with fuzzy numbers……Page 169
8.4 Conclusion……Page 177
9.1 Introduction……Page 179
9.2 Job-shop scheduling……Page 181
9.3 Genetic algorithms for job-shop scheduling……Page 184
10.1 Introduction……Page 199
10.2 Job-shop scheduling with fuzzy processing time and fuzzy due date……Page 201
10.3 Multiobjective job-shop scheduling under fuzziness……Page 218
11.1 Flexible scheduling in a machining center……Page 233
11.2 Operation planning of district heating and cooling plants……Page 246
11.3 Coal purchase planning in electric power plants……Page 263
References……Page 283
Index……Page 297
Back cover……Page 299