SNDL-MOEA: "Stored Non-Domination Level MOEA"


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.

Meeting Name

9th Annual Conference on Genetic and Evolutionary Computation (2007: July 7-11, London, England)


Computer Science

Keywords and Phrases

Constructive Problem Solving; Evolutionary Multiobjective Optimization; Pareto Optimality

Document Type

Article - Conference proceedings

Document Version


File Type





© 2007 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

11 Jul 2007