"Reliability of convergence metric and hypervolume indicator for many-o" by Monalisa Pal and Sanghamitra Bandyopadhyay
 

Reliability of convergence metric and hypervolume indicator for many-objective optimization

Document Type

Conference Article

Publication Title

2016 2nd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2016

Abstract

With the emergence and growth of Many-Objective Optimization algorithms, there has been an increased necessity to formulate new metrics that can perform quantitative assessment of the Pareto-Front returned as a solution from a Many-Objective Optimization algorithm. Out of the many evaluation metrics in use, convergence metric and hypervolume indicator have gained immense attention. This paper demonstrates how optimality obtained with respect to one or both of these metrics can be misleading at times. The demonstration is done in two-dimensional scenarios which suggests that the disadvantages of these metrics can be more pronounced when the applications are in higher dimensional space which not only has scalability issues but also where visualization of the space is not feasible. The paper is concluded stating the need for efficient evaluation metric which will accumulate information from the Pareto-Front in terms of convergence, diversity, number of solution (discarding outliers) and shape of the surface.

First Page

511

Last Page

515

DOI

10.1109/CIEC.2016.7513806

Publication Date

7-14-2016

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