Quarterly (spring, summer, fall, winter)
176 pp. per issue
7 x 10
2014 Impact factor:

Evolutionary Computation

Fall 2015, Vol. 23, No. 3, Pages 369-395.
(doi: 10.1162/EVCO_a_00135)
© 2015 Massachusetts Institute of Technology
2 Indicator-Based Multiobjective Search
Article PDF (1.38 MB)

In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The and the hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the indicator exist. In this extended version of our previous conference paper, we thus perform a comprehensive investigation of the properties of the indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the and HV indicator are presented. Furthermore, the indicator is integrated into an indicator-based steady-state evolutionary multiobjective optimization algorithm (EMOA). It is shown that the so-called -EMOA can accurately approximate the optimal distribution of solutions regarding .