23 Papers
69 Citations
Lei Chen is an academic researcher from Guangdong University of Technology. The author has contributed to research in topics: Evolutionary algorithm & Population. The author has an hindex of 9, co-authored 23 publications.
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Papers
Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties
TL;DR: A set of ten new test problems with above-mentioned difficulties are constructed and some experimental results on these test problems found by two popular EMO algorithms, i.e., MOEA/D and NSGA-III, are reported and analyzed.
155
Investigating the Effect of Imbalance Between Convergence and Diversity in Evolutionary Multiobjective Algorithms
TL;DR: This paper characterizes an imbalanced MOP by clearly defining properties and indicating the reasons for the existing EMO algorithms’ difficulties in solving them, and presents 14 imbalanced problems, with and without constraints.
102
Evolutionary Many-Objective Algorithm Using Decomposition-Based Dominance Relationship
TL;DR: It is theoretically proved that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected, and it is shown that highly customized dominance relationship can be derived from decomposition for any given decomposition vector.
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An evolutionary many-objective optimisation algorithm with adaptive region decomposition
Hai-Lin Liu,Lei Chen,Qingfu Zhang,Kalyanmoy Deb +3 more
- 24 Jul 2016
TL;DR: An adaptive region decomposition framework: MOEA/D-AM2M for the degenerated Many-Objective optimization problem (MaOP), where degenerated MaOP refers to the optimization problem with a degenerated PF in a subspace of the objective space.
42
Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition-Based Evolutionary Many-Objective Optimization Algorithms.
TL;DR: This article makes 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 and makes important theoretical conclusions on PBI -based decomposition algorithms derived from the study.
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