Abstract
Multi-objective Optimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "Many-Objective Optimization Problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a Multi-Objective Evolutionary Algorithm (MOEA) and its MaOP counterpart.
Recommended Citation
D. M. Curry and C. H. Dagli, "Computational Complexity Measures for Many-objective Optimization Problems," Procedia Computer Science, vol. 36, pp. 185 - 191, Elsevier, Jan 2014.
The definitive version is available at https://doi.org/10.1016/j.procs.2014.09.077
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Computational complexity; Many-objective optimization problem; MaOP; MOEA; MOP; Multi-objective optimization problem; Multiobjective evolutionary algorithm
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Jan 2014
