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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6439631, 20 pages
Research Article

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Air Force Engineering University, Xi’an, China

Correspondence should be addressed to Jiale Gao; moc.361@dgk_elaijoag

Received 4 July 2017; Revised 18 October 2017; Accepted 15 November 2017; Published 17 December 2017

Academic Editor: Salvatore Alfonzetti

Copyright © 2017 Jiale Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.