Performance measures for dynamic multi-objective optimisation algorithms

@article{Helbig2013PerformanceMF,
  title={Performance measures for dynamic multi-objective optimisation algorithms},
  author={Mard{\'e} Helbig and Andries Petrus Engelbrecht},
  journal={Inf. Sci.},
  year={2013},
  volume={250},
  pages={61-81}
}
When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), performance measures are required to quantify the performance of the algorithm and to compare one algorithm’s performance against that of other algorithms. However, for dynamic multiobjective optimisation (DMOO) there are no standard performance measures. This article provides an overview of the performance measures that have been used so far. In addition, issues with performance measures that are currently being used… CONTINUE READING
Highly Cited
This paper has 49 citations. REVIEW CITATIONS
24 Citations
75 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 24 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 75 references

Multiobjective evolutionary algorithms: classification, analyses, and new innovations

  • D. A. van Veldhuizen
  • Ph.D. thesis, Graduate School of Engineering Air…
  • 1999
Highly Influential
6 Excerpts

Fault tolerance design using single and multi-criteria genetic algorithms

  • J. R. Schott
  • Master’s thesis, Department of Aeronautics and…
  • 1995
Highly Influential
7 Excerpts

Performance evaluation of evolutionary heuristics in dynamic environments

  • D. Ayvaz, F. Gurgen
  • Applied Intelligence 37 (1)
  • 2012
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…