Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition

Adaptive operator selection (AOS) is used to determine the application rates of different operators in an online manner based on their recent performances within an optimization process. This paper proposes a bandit-based AOS method, fitness-rate-rank-based multiarmed bandit (FRRMAB). In order to tr...

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Vydané v:IEEE transactions on evolutionary computation Ročník 18; číslo 1; s. 114 - 130
Hlavní autori: Ke Li, Fialho, Alvaro, Kwong, Sam, Qingfu Zhang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.02.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive operator selection (AOS)
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Computer science; control theory; systems
Decay
Decision theory. Utility theory
Decomposition
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Finite impulse response filter
Fitness
Game theory
Heuristic algorithms
multiarmed bandit
multiobjective evolutionary algorithm based on decomposition (MOEA/D)
multiobjective optimization
On-line systems
Operational research and scientific management
Operational research. Management science
Operators
Optimization
Pareto optimization
Robustness
Scalars
Theoretical computing
Vectors
Probabilistic approach
Pareto optimum
multiarmed bandit
Process planning
Evolutionary algorithm
Multiobjective programming
Dominance
decomposition
Gambling game
Adaptive method
Optimization
multiobjective evolutionary algorithm based on decomposition (MOEA/D)
multiobjective optimization
Experimental result
Sliding window
Adaptive operator selection (AOS)
Bandit process
Mathematical programming
ISSN:1089-778X, 1941-0026
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Shrnutí:Adaptive operator selection (AOS) is used to determine the application rates of different operators in an online manner based on their recent performances within an optimization process. This paper proposes a bandit-based AOS method, fitness-rate-rank-based multiarmed bandit (FRRMAB). In order to track the dynamics of the search process, it uses a sliding window to record the recent fitness improvement rates achieved by the operators, while employing a decaying mechanism to increase the selection probability of the best operator. Not much work has been done on AOS in multiobjective evolutionary computation since it is very difficult to measure the fitness improvements quantitatively in most Pareto-dominance-based multiobjective evolutionary algorithms. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Thus, it is natural and feasible to use AOS in MOEA/D. We investigate several important issues in using FRRMAB in MOEA/D. Our experimental results demonstrate that FRRMAB is robust and its operator selection is reasonable. Comparison experiments also indicate that FRRMAB can significantly improve the performance of MOEA/D.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2013.2239648