Proceedings ArticleDOI
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
Qingfu Zhang,Wudong Liu,Hui Li +2 more
- pp 203-208
TLDR
The new version of MOEA/D has been tested on all the CEC09 unconstrained MOP test instances and a strategy for allocating the computational resource to different subproblems in MOEA /D is proposed.Abstract:
This paper describes the idea of MOEA/D and proposes a strategy for allocating the computational resource to different subproblems in MOEA/D. The new version of MOEA/D has been tested on all the CEC09 unconstrained MOP test instances.read more
Citations
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Journal ArticleDOI
Multiobjective evolutionary algorithms: A survey of the state of the art
TL;DR: This paper surveys the development ofMOEAs primarily during the last eight years and covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEas, coevolutionary MOE As, selection and offspring reproduction operators, MOE as with specific search methods, MOeAs for multimodal problems, constraint handling and MOE
Journal ArticleDOI
Recent advances in differential evolution – An updated survey
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
Journal ArticleDOI
Many-Objective Evolutionary Algorithms: A Survey
TL;DR: A survey of MaOEAs is reported and seven classes of many-objective evolutionary algorithms proposed are categorized into seven classes: relaxed dominance based, diversity-based, aggregation- based, indicator-Based, reference set based, preference-based and dimensionality reduction approaches.
Journal ArticleDOI
Borg: An auto-adaptive many-objective evolutionary computing framework
David Hadka,Patrick M. Reed +1 more
TL;DR: The Borg MOEA combines -dominance, a measure of convergence speed named -progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework for many-objective, multimodal optimization.
Journal ArticleDOI
Moea/d with adaptive weight adjustment
TL;DR: Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Journal ArticleDOI
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Book
Nonlinear Multiobjective Optimization
TL;DR: This paper is concerned with the development of methods for dealing with the role of symbols in the interpretation of semantics.
Journal ArticleDOI
Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II
Hui Li,Qingfu Zhang +1 more
TL;DR: The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances, and suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes.