  In evolutionary computation, different reproduction operators have various
search dynamics. To strike a well balance between exploration and exploitation,
it is attractive to have an adaptive operator selection (AOS) mechanism that
automatically chooses the most appropriate operator on the fly according to the
current status. This paper proposes a new AOS mechanism for multi-objective
evolutionary algorithm based on decomposition (MOEA/D). More specifically, the
AOS is formulated as a multi-armed bandit problem where the dynamic Thompson
sampling (DYTS) is applied to adapt the bandit learning model, originally
proposed with an assumption of a fixed award distribution, to a non-stationary
setup. In particular, each arm of our bandit learning model represents a
reproduction operator and is assigned with a prior reward distribution. The
parameters of these reward distributions will be progressively updated
according to the performance of its performance collected from the evolutionary
process. When generating an offspring, an operator is chosen by sampling from
those reward distribution according to the DYTS. Experimental results fully
demonstrate the effectiveness and competitiveness of our proposed AOS mechanism
compared with other four state-of-the-art MOEA/D variants.
