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Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction

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Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

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).

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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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  1. Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, Uttar Pradesh, 208016, India

    Kalyanmoy Deb

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  1. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Lihui Wang

  2. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Amos H. C. Ng

  3. , Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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Deb, K. (2011). Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction. In: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8_1

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