SNDL-MOEA: "Stored Non-Domination Level MOEA"
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.
Recommended Citation
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
Meeting Name
9th Annual Conference on Genetic and Evolutionary Computation (2007: July 7-11, London, England)
Department(s)
Computer Science
Keywords and Phrases
Constructive Problem Solving; Evolutionary Multiobjective Optimization; Pareto Optimality
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2007 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
11 Jul 2007
