Multi-Objective Optimization Using Evolutionary Algorithms

Description

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.Comprehensive coverage of this growing area of researchCarefully introduces each algorithm with examples and in-depth discussionIncludes many applications to real-world problems, including engineering design and schedulingIncludes discussion of advanced topics and future researchCan be used as a course text or for self-studyAccessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithmsThe integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study. Foreword xv Preface xvii 1 Prologue 1 2 Multi-Objective Optimization 13 3 Classical Methods 49 4 Evolutionary Algorithms 81 5 Non-Elitist Multi-Objective Evolutionary Algorithms 171 6 Elitist Multi-Objective Evolutionary Algorithms 239 7 Constrained Multi-Objective Evolutionary Algorithms 289 8 Salient Issues of Multi-Objective Evolutionary Algorithms 315 9 Applications of Multi-Objective Evolutionary Algorithms 447 10 Epilogue 481 References 489 Index 509