Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a recently proposed algorithm which is a research focus in the field of multi-objective evolutionary optimization. It decomposes a multi-objective problem into subproblems by mathematic programming methods and applies evolutio...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 22; číslo 12; s. 3997 - 4012
Hlavní autoři: Zhang, Chunjiang, Tan, Kay Chen, Lee, Loo Hay, Gao, Liang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018
Springer Nature B.V
Témata:
Artificial Intelligence
Computational Intelligence
Control
Decomposition
Engineering
Evolutionary algorithms
Genetic algorithms
Interpolation
Mathematical Logic and Foundations
Mathematical programming
Mechatronics
Methodologies and Application
Methods
Multiple objective analysis
Optimization
Robotics
Sparsity
Adjust weight vector
Multi-objective evolutionary algorithm (MOEA)
MOEA/D
Discontinuous Pareto fronts
ISSN:1432-7643, 1433-7479
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Shrnutí:Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a recently proposed algorithm which is a research focus in the field of multi-objective evolutionary optimization. It decomposes a multi-objective problem into subproblems by mathematic programming methods and applies evolutionary algorithms to optimize the subproblems simultaneously. MOEA/D is good at finding Pareto solutions which are evenly distributed. However, it can be improved for problems with discontinuous Pareto fronts (PF). Many solutions will assemble in breakpoints in this situation. A method for adjusting weight vectors for bi-objective optimization problems with discontinuous PF is proposed. Firstly, this method detects the weight vectors which need to be adjusted using a property of MOEA/D. Secondly, the reserved vectors are divided into several subsets. Thirdly, after calculating the ideal number of vectors in each subset, vectors are adjusted evenly. Lastly, the corresponding solutions are updated by a linear interpolation. Numerical experiment shows the proposed method obtains good diversity and convergence on approached PF.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-017-2609-4