An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods

Li, Miqing1; Yao, Xin1,2

10.1007/978-3-030-12598-1_2

2019

16113349

10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019

11411 LNCS

15-26

East Lansing, MI, United states

Springer Verlag

Most evolutionary multiobjective optimisation (EMO) algorithms explicitly or implicitly maintain an archive for an approximation of the Pareto front. A question arising is whether existing archiving methods are reliable with respect to their convergence and approximation ability. Despite theoretical results available, it remains unknown how these archivers actually perform in practice. In particular, what percentage of solutions in their final archive are Pareto optimal? How frequently do they experience deterioration during the archiving process? Deterioration means archiving a new solution which is dominated by some solution discarded previously. This paper answers the above questions through a systematic investigation of eight representative archivers on 37 test instances with two to five objectives. We have found that (1) deterioration happens to all the archivers; (2) the deterioration degree can vary dramatically on different problems; (3) some archivers clearly perform better than others; and (4) several popular archivers sometime return a population with most solutions being the non-optimal. All of these suggest the need of improvement of current archiving methods.
© Springer Nature Switzerland AG 2019.

其他

EI

Engineering and Physical Sciences Research Council[EP/J017515/1] ; Engineering and Physical Sciences Research Council[EP/P005578/1]

20191206656983

Approximation algorithms ; Deterioration ; Evolutionary algorithms ; Pareto principle

Mathematics:921 ; Optimization Techniques:921.5 ; Materials Science:951

EV Compendex

1.CERCIA, School of Computer Science, University of Birmingham, Birmingham; B15 2TT, United Kingdom
2.Shenzhen Key Laboratory of Computational Intelligence (SKyLoCI), Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Li, Miqing,Yao, Xin. An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods[C]:Springer Verlag,2019:15-26.