Abstract
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.
Highlights
Multiobjective optimization is a common problem that scientists and engineers face, which concerns optimizing problems with multiple and often conflicting objectives
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)
Its adaptive parameter control strategy is to allocate probabilities for SBX and differential operator (DE) according to the search period, whereas Zhao et al [20] suggest that the different neighborhood sizes have an unavoidable influence on the search power of operators based on the framework of multiobjective evolutionary algorithms (MOEAs)/D, and the experiments imply that adaptive selection of neighborhood sizes works very well
Summary
Multiobjective optimization is a common problem that scientists and engineers face, which concerns optimizing problems with multiple and often conflicting objectives. Its adaptive parameter control strategy is to allocate probabilities for SBX and DE according to the search period, whereas Zhao et al [20] suggest that the different neighborhood sizes have an unavoidable influence on the search power of operators based on the framework of MOEA/D, and the experiments imply that adaptive selection of neighborhood sizes works very well. The upper confidence bound (UCB) selection strategy, has been used to solve the EvE dilemma since 1994 [22] It possesses distinctive advantages among many AOSs, and a host of improved UCB versions appears after that. Enlightened by the sliding time window, we present a novel adaptive method which is used to store the information about the operators, called latest stored information based adaptive selection (LSIAS) strategy. Conclusions are summarized and further work along the direction of the adaptive selection is discussed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
