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Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction

  • Kalyanmoy Deb
Chapter

Abstract

As the name suggests, multi-objective optimisation involves optimising a number of objectives simultaneously. The problem becomes challenging when the objectives are of conflicting characteristics to each other, that is, the optimal solution of an objective function is different from that of the other. In the course of solving such problems, with or without the presence of constraints, these problems give rise to a set of trade-off optimal solutions, popularly known as Pareto-optimal solutions. Because of the multiplicity in solutions, these problems were proposed to be solved suitably using evolutionary algorithms using a population approach in its search procedure. Starting with parameterised procedures in early 90s, the so-called evolutionary multi-objective optimisation (EMO) algorithms is now an established field of research and application with many dedicated texts and edited books, commercial softwares and numerous freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. In this chapter, we provide a brief introduction to its operating principles and outline the current research and application studies of evolutionary multi-objective optmisation (EMO).

Keywords

Population Member Multiple Criterion Decision Making Multiple Criterion Decision Making Approach Evolutionary Optimisation Procedure Decison Maker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The author acknowledges the support and his association with University of Skövde, Sweden and Aalto University School of Economics, Helsinki. This chapter contains some excerpts from previous publications by the same author entitled ‘Introduction to Evolutionary Multi-Objective optimisation’, in J. Branke, K. Deb, K. Miettinen and R. Slowinski (Eds.) Multiobjective Optimization: Interactive and Evolutionary Approaches (LNCS 5252) (pp. 59–96), 2008, Berlin: Springer and ‘Recent Developments in Evolutionary Multi-Objective Optimization’ in M. Ehrgott et al. (Eds.) Trends in Multiple Criteria Decision Analysis (pp. 339-368), 2010, Berlin: Springer.

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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of TechnologyKanpurIndia

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