Scalarizing cost‐effective multi‐objective optimization algorithms made possible with kriging

Purpose The purpose of this paper is threefold: to make explicitly clear the range of efficient multi‐objective optimization algorithms which are available with kriging; to demonstrate a previously uninvestigated algorithm on an electromagnetic design problem; and to identify algorithms particularly worthy of investigation in this field. Design/methodology/approach The paper concentrates exclusively on scalarizing multi‐objective optimization algorithms. By reviewing the range of selection criteria based on kriging models for single‐objective optimization along with the range of methods available for transforming a multi‐objective optimization problem to a single‐objective problem, the family of scalarizing multi‐objective optimization algorithms is made explicitly clear. Findings One of the proposed algorithms is demonstrated on the multi‐objective design of an electron gun. It is able to identify efficiently an approximation to the Pareto‐optimal front. Research limitations/implications The algorithms proposed are applicable to unconstrained problems only. One future development is to incorporate constraint‐handling techniques from single‐objective optimization into the scalarizing algorithms. Originality/value A family of algorithms, most of which have not been explored before in the literature, is proposed. Algorithms of particular potential (utilizing the most promising developments in single‐objective optimization) are identified.