Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decompositi...

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Published inMathematical problems in engineering Vol. 2017; no. 2017; pp. 1 - 20
Main Authors Liang, Zhibing, Fan, Chengli, Xing, Qinghua, Gao, Jiale
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
Hindawi Limited
Subjects
Abandonment
Adaptive algorithms
Adaptive control
Artificial intelligence
Cybernetics
Decomposition
Evolutionary algorithms
Fitness
Genetic algorithms
Methods
Multiple objective analysis
Mutation
Neighborhoods
Operators
Performance enhancement
Power
Probability
Searching
Strategy
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Summary:The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems.
ISSN:1024-123X
1563-5147
DOI:10.1155/2017/6439631