MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 11; no. 6; pp. 712 - 731
Main Authors: Qingfu Zhang, Qingfu Zhang, Hui Li, Hui Li
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.12.2007
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Artificial intelligence
Computational complexity
Computer science
Computer science; control theory; systems
Decomposition
Evolutionary
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Genetic algorithms
Knapsack problem
Learning and adaptive systems
Mathematical model
Mathematical models
multiobjective optimization
Optimization
Optimization methods
Pareto optimality
Pareto optimization
Scalability
Scalars
Sorting
Strategy
Studies
Testing
multiobjective optimization
Pareto optimality
decomposition
Computational complexity
evolutionary algorithm
Scalability
Pareto optimum
Evolutionary algorithm
Multiobjective programming
Optimization
Knapsack problem
Sorting
Genetic algorithm
Mathematical programming
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). 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 multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2007.892759