On the use of hypervolume for diversity measurement of Pareto front approximations

Jiang, Shouyong, Yang, S. and Li, M. (2017) On the use of hypervolume for diversity measurement of Pareto front approximations. In: 2016 IEEE Symposium Series on Computational Intelligence, 6-9th December 2016, Athens, Greece.

Full content URL: http://doi.org/10.1109/SSCI.2016.7850225

Full text not available from this repository.

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


In multiobjective optimization, a good quality indicator is of great importance to the performance assessment of algorithms. This paper investigates the effectiveness of the widely-used hypervolume indicator, which is the only one found so far to strictly comply with the Pareto dominance. While hypervolume is of undisputed success to assess the quality of an approximation, it is sensitive to misleading cases, particularly for diversity assessment. To address this issue, this paper presents a modified hypervolume indicator based on linear projection for diversity evaluation. In addition to experimental studies to demonstrate the effectiveness of the proposed indicator, the indicator is introduced into the environmental selecction of an indicator-based multiobjective optimization evolutionary algorithm. Experiments show that the proposed indicator yields more evenly-distributed approximations than the original hypervolume indicator.

Additional Information:cited By 2; Conference of 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference Date: 6 December 2016 Through 9 December 2016; Conference Code:126460
Keywords:Artificial intelligence, Multiobjective optimization, Optimization, Diversity measurement, Hypervolume indicators, Linear projections, Multi-objective optimization evolutionary algorithms, Pareto dominance, Pareto front, Performance assessment, Quality indicators, Pareto principle
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35665
Deposited On:01 May 2019 10:39

Repository Staff Only: item control page