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.
M. D. Johnson et al., "SNDL-MOEA: "Stored Non-Domination Level MOEA"," Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (2007, London, England), Association for Computing Machinery (ACM), Jul 2007.
The definitive version is available at https://doi.org/10.1145/1276958.1277123
9th Annual Conference on Genetic and Evolutionary Computation (2007: July 7-11, London, England)
Keywords and Phrases
Constructive Problem Solving; Evolutionary Multiobjective Optimization; Pareto Optimality
Article - Conference proceedings
© 2007 Association for Computing Machinery (ACM), All rights reserved.
11 Jul 2007