Multi-objective Optimization

Kalyanmoy Deb () and Kalyanmoy Deb
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Kalyanmoy Deb: Michigan State University
Kalyanmoy Deb: Michigan State University

Chapter Chapter 15 in Search Methodologies, 2014, pp 403-449 from Springer

Abstract: Abstract Multi-objective optimization is an integral part of optimization activities and has a tremendous practical importance, since almost all real-world optimization problems are ideally suited to be modeled using multiple conflicting objectives. The classical means of solving such problems were primarily focused on scalarizing multiple objectives into a single objective, whereas the evolutionary means have been to solve a multi-objective optimization problem as it is. In this chapter, we discuss the fundamental principles of multi-objective optimization, the differences between multi-objective optimization and single-objective optimization, and describe a few well-known classical and evolutionary algorithms for multi-objective optimization. Two application case studies reveal the importance of multi-objective optimization in practice. A number of research challenges are then highlighted. The chapter concludes by suggesting a few tricks of the trade and mentioning some key resources to the field of multi-objective optimization.

Keywords: Multiobjective Optimization; Archive Member; Domination Count; Ideal Objective Vector; Nadir Objective Vector (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (19)

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DOI: 10.1007/978-1-4614-6940-7_15

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