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Multi-Objective Optimization

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Search Methodologies

Abstract

Many real-world search and optimization problems are naturally posed as non-linear programming problems having multiple objectives. Due to the lack of suitable solution techniques, such problems were artificially converted into a single-objective problem and solved. The difficulty arose because such problems give rise to a set of trade-off optimal solutions (known as Pareto-optimal solutions), instead of a single optimum solution. It then becomes important to find not just one Pareto-optimal solution, but as many of them as possible. This is because any two such solutions constitutes a trade-off among the objectives and users would be in a better position to make a choice when many such trade-off solutions are unveiled.

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Authors and Affiliations

  1. Kanpur Genetic Algorithms Laboratory (KanGAL), Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India

    Kalyanmoy Deb

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  1. Kalyanmoy Deb
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  1. University of Nottingham, UK

    Edmund K. Burke  & Graham Kendall  & 

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Deb, K. (2005). Multi-Objective Optimization. In: Burke, E.K., Kendall, G. (eds) Search Methodologies. Springer, Boston, MA. https://doi.org/10.1007/0-387-28356-0_10

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