A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems | Zendy

Indraneel Das | Zendy; J. E. Dennis | Zendy

Open Access

A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems

Author(s) -

Indraneel Das,

J. E. Dennis

Publication year - 1997

Publication title -

structural optimization

Language(s) - English

Resource type - Journals

eISSN - 1436-2503

pISSN - 0934-4373

DOI - 10.1007/bf01197559

Subject(s) - pareto principle , pareto interpolation , mathematical optimization , mathematics , pareto distribution , multi objective optimization , pareto analysis , set (abstract data type) , regular polygon , lomax distribution , distribution (mathematics) , computer science , generalized pareto distribution , statistics , extreme value theory , geometry , mathematical analysis , programming language

A standard technique for generating the Pareto set in multicriteria optimization problems is to minimize (convex) weighted sums of the different objectives for various different settings of the weights. However, it is well-known that this method succeeds in getting points from all parts of the Pareto set only when the Pareto curve is convex. This article provides a geometrical argument as to why this is the case.

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