Operating system

Algorithm

Mathematical optimization

Local search (optimization)

Geometry

Key (lock)

Space (punctuation)

Point (geometry)

Mathematics

Computer security

Computer science

Computation

European Journal of Operational Research

Multi-objective optimization procedures usually proceed by iteratively producing new solutions. For this purpose, a key issue is to determine and efficiently update the search region, which corresponds to the part of the objective space where new nondominated points could lie. In this paper we elaborate a specific neighborhood structure among local upper bounds. Thanks to this structure, the update of the search region with respect to a new point can be performed more efficiently compared to existing approaches. Moreover, the neighborhood structure provides new insight into the search region and the location of nondominated points.

Efficient computation of the search region in multi-objective optimization