SNDL-MOEA: stored non-domination level MOEA

Authors:
Matt D. Johnson;Daniel R. Tauritz;Ralph W. Wilkerson
Affiliations:
University of Missouri-Rolla, Rolla, MO;University of Missouri-Rolla, Rolla, MO;University of Missouri-Rolla, Rolla, MO
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

Citing 6
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Abstract

There exist a number of high-performance Multi-Objective Evolutionary Algorithms (MOEAs) for solving Multi-Objective Optimization (MOO) problems; two of the best are NSGA-II and epsilon-MOEA. However, they lack an archive population sorted into levels of non-domination, making them unsuitable for construction problems where some type of backtracking to earlier intermediate solutions is required. In this paper we introduce our Stored Non-Domination Level (SNDL) MOEA for solving such construction problems. SNDL-MOEA combines some of the best features of NSGA-II and epsilon-MOEA with the ability to store and recall intermediate solutions necessary for construction problems. We present results for applying SNDL-MOEA to the Tight Single Change Covering Design (TSCCD) construction problem, demonstrating its applicability. Furthermore, we show with a detailed performance comparison between SNDL-MOEA, NSGA-II, and epsilon-MOEA on two standard test series that SNDL-MOEA is capable of outperforming NSGA-II and is competitive with epsilon-MOEA.