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Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition-Based Evolutionary Many-Objective Optimization Algorithms.
- Source :
-
Evolutionary Computation . Spring2021, Vol. 29 Issue 1, p157-186. 30p. - Publication Year :
- 2021
-
Abstract
- An objective normalization strategy is essential in any evolutionary multiobjective or many-objective optimization (EMO or EMaO) algorithm, due to the distance calculations between objective vectors required to compute diversity and convergence of population members. For the decomposition-based EMO/EMaO algorithms involving the Penalty Boundary Intersection (PBI) metric, normalization is an important matter due to the computation of two distance metrics. In this article, we make a theoretical analysis of the effect of instabilities in the normalization process on the performance of PBI-based MOEA/D and a proposed PBI-based NSGA-III procedure. Although the effect is well recognized in the literature, few theoretical studies have been done so far to understand its true nature and the choice of a suitable penalty parameter value for an arbitrary problem. The developed theoretical results have been corroborated with extensive experimental results on three to 15-objective convex and non-convex instances of DTLZ and WFG problems. The article, makes important theoretical conclusions on PBI-based decomposition algorithms derived from the study. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10636560
- Volume :
- 29
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Evolutionary Computation
- Publication Type :
- Academic Journal
- Accession number :
- 149005804
- Full Text :
- https://doi.org/10.1162/evco_a_00276