Genetic Algorithms and Engineering Optimization (1999) [Gen Cheng] [9780471315315]

518 Pages·15.186 MB·English

Genetic
Algorithms
& Engineering
Optimization
Mitsuo Gen
Runwei Cheng
GENETIC ALGORITHMS AND
ENGINEERING OPTIMIZATION
Genetic Algorithms and Engineering Optimization. Mitsuo Gen and Runwei Cheng
Copyright © 2000 John Wiley & Sons, Inc.
WILEY SERIES IN ENGINEERING DESIGN
AND AUTOMATION
Series Editor
HAMID R. PARSAEI
GENETIC ALGORITHMS AND ENGINEERING DESIGN
Mitsuo Gen and Runwei Cheng
ADVANCED TOLERANCING TECHNIQUES
Hong-Chao Zhang
INTEGRATED PRODUCT AND PROCESS
DEVELOPMENT: METHODOLOGIES, TOOLS, AND
TECHNOLOGIES
John M. Usher, Utpal Roy, and Hamid R. Parsaei
GENETIC ALGORITHMS AND ENGINEERING
OPTIMIZATION
Mitsuo Gen and Runwei Cheng
GENETIC ALGORITHMS
AND ENGINEERING
OPTIMIZATION
MITSUO GEN
RUNWEI CHENG
Ashikaga Institute of Technology
Ashikaga, Japan
A Wiley-Interscience Publication
JOHN WILEY & SONS, INC.
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library of Congress Cataloging-in-Publication Data:
Gen, Mitsuo, 1944-
Genetic algorithms and engineering optimization / by Mitsuo Gen,
Runwei Cheng.
p. cm.
“A Wiley-Interscience publication.”
Includes bibliographical references and index.
ISBN 0-471-31531-1 (alk. paper)
1. Industrial engineering—Mathematical models. 2. Genetic
algorithms. 3. Mathematical optimization. I. Cheng, Runwie.
II. Title.
T56.24.G46 2000
670'.285'51— dc21 99-16023
To Our Fathers, Mothers, and Families
CONTENTS
Preface xiii
1 Foundations of Genetic Algorithms 1
1.1 Introduction 1
1.1.1 Encoding Issue 2
1.1.2 Genetic Operators 7
1.1.3 Selection 9
1.1.4 Genetic Local Search 11
1.2 Adaptation of Genetic Algorithms 14
1.2.1 Structure Adaptation 15
1.2.2 Parameter Adaptation 16
1.2.3 Fuzzy Logic Controller 18
1.3 Genetic Optimizations 27
1.3.1 Global Optimizations 27
1.3.2 Constrained Optimizations 34
1.3.3 Combinatorial Optimizations 38
1.3.4 Multiobjective Optimizations 39
1.4 Recent Genetic Algorithm Dissertations 40
2 Combinatorial Optimization Problems 53
2.1 Introduction 53
2.2 Set-Covering Problem 53
2.2.1 Airline Crew Scheduling Problems 56
2.2.2 Genetic Representation 56
2.2.3 Genetic Operators 58
2.2.4 Genetic Algorithm 60
2.2.5 Computational Experience 61
2.3 Bin-Packing Problem 61
2.3.1 Heuristic Algorithms 63
vii
viii CONTENTS
2.3.2 Genetic Representation 65
2.3.3 Genetic Operators 68
2.3.4 Fitness Function 69
2.3.5 Initial Population 69
2.3.6 Computational Experience 70
2.4 Knapsack Problem 71
2.4.1 Multiple-Choice Knapsack Problem 72
2.4.2 Multiconstraint Knapsack Problem 77
2.5 Minimum Spanning Tree Problem 81
2.5.1 Quadratic Minimum Spanning Tree Problem 82
2.5.2 Degree-Constrained Minimum Spanning Tree Problem 85
2.5.3 Bicriteria Minimum Spanning Tree Problem 90
3 Multiobjective Optimization Problems 97
3.1 Introduction 97
3.2 Basic Concepts of Multiobjective Optimizations 97
3.2.1 Nondominated Solutions 98
3.2.2 Preference Structures 101
3.2.3 Basic Solution Approaches 102
3.2.4 Structures and Properties of Problems 106
3.3 Genetic Mulitobjective Optimization 106
3.3.1 Features of Genetic Search 106
3.3.2 Fitness Assignment Mechanism 107
3.3.3 Fitness Sharing and Population Diversity 111
3.3.4 The Concept of Pareto Solution 113
3.4 Vector-Evaluated Genetic Algorithms 115
3.5 Pareto Ranking and Tournament Methods 118
3.5.1 Pareto Ranking Method 118
3.5.2 Pareto Tournament Method 122
3.6 Weighted-Sum Approach 124
3.6.1 Random-Weight Approach 125
3.6.2 Adaptive Weight Approach 127
3.7 Distance Method 131
3.7.1 General Idea of the Distance Method 131
3.7.2 Calculation of Distance Measure 133
3.7.3 Application of the Distance Method 136
3.8 Compromise Approach 136
3.9 Goal Programming Approach 138
CONTENTS ix
4 Fuzzy Optimization Problems 142
4.1 Introduction 142
4.2 Fuzzy Linear Programming 143
4.2.1 Fuzzy Linear Programming Model 143
4.2.2 Genetic Algorithm Approach 149
4.2.3 Interactive Approach 152
4.2.4 Numerical Example 154
4.3 Fuzzy Nonlinear Programming 156
4.3.1 Nonlinear Programming Model 157
4.3.2 Inexact Approach to FO/RNP-1 161
4.3.3 Interactive Approach 163
4.3.4 Numerical Example 164
4.4 Fuzzy Nonlinear Mixed-Integer Goal Programming 165
4.4.1 Fuzzy Nonlinear Mixed-Integer Goal Programming
Model 168
4.4.2 Genetic Algorithm Approach 170
4.4.3 Numerical Examples 173
4.5 Fuzzy Multiobjective Integer Programming 178
4.5.1 Problem Formulation 181
4.5.2 Augmented Minimax Problems 184
4.5.3 Genetic Algorithm Approach 185
4.5.4 Interactive Fuzzy Satisfaction Method 189
4.5.5 Numerical Example 190
5 Reliability Design Problems 194
5.1 Introduction 194
5.2 Network Reliability Design 195
5.2.1 Problem Formulation 196
5.2.2 Dengiz, Altiparmak, and Smith’s Approach 198
5.2.3 Deeter and Smith’s Approach 204
5.3 Tree-Based Network Reliability and LAN Design 211
5.3.1 Bicriteria Network Topology Design 212
5.3.2 Numerical Examples 219
5.4 Multiobjective Reliability Design 221
5.4.1 Bicriteria Reliability Design 221
5.4.2 Genetic Algorithm Approach 224
5.4.3 Hybrid Genetic Algorithm Approach 226
5.4.4 Reliability Design with Fuzzy Goals 230
X CONTENTS
6 Scheduling Problems 235
6.1 Introduction 235
6.2 Job-Shop Scheduling 235
6.2.1 Basic Approaches 236
6.2.2 Encodings 236
6.2.3 Adapted Genetic Operators 238
6.2.4 Heuristic-Featured Genetic Operators 242
6.2.5 Hybrid Genetic Algorithms 244
6.2.6 Discussion 251
6.3 Grouped Job Scheduling Problem 253
6.3.1 Problem Description and Necessary Condition 253
6.3.2 Fundamental Runs 255
6.3.3 Representation 257
6.3.4 Evaluation 259
6.3.5 Genetic Operators 260
6.3.6 Overall Procedure 260
6.3.7 Numerical Example 261
6.4 Resource-Constrained Project Scheduling 263
6.4.1 Priority-Based Encoding 265
6.4.2 Genetic Operators 270
6.4.3 Evaluation and Selection 272
6.4.4 Experimental Results 274
6.5 Parallel Machine Scheduling 278
6.5.1 Dominance Condition 280
6.5.2 Memetic Algorithms 284
6.5.3 Experimental Results 287
6.6 Multiprocessor Scheduling 288
6.6.1 Problem Description and Assumptions 289
6.6.2 Genetic Algorithm for MSP 290
6.6.3 Numerical Example 295
7 Advanced Transportation Problems 297
7.1 Introduction 297
7.1.1 Transportation Model 297
7.1.2 Formulation of Transportation Problems 299
7.2 Spanning Tree-Based Approach 304
7.2.1 Tree Representation 304
7.2.2 Initialization 306
7.2.3 Genetic Operators 308