Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D

TL;DR

This paper introduces a novel adaptive operator selection mechanism for MOEA/D using dynamic Thompson sampling, which effectively balances exploration and exploitation in evolutionary algorithms, leading to improved performance.

Contribution

It proposes a new AOS method based on dynamic Thompson sampling for MOEA/D, adapting to non-stationary environments and outperforming existing methods.

Findings

Demonstrates the effectiveness of the proposed AOS mechanism.

Shows improved performance over four state-of-the-art MOEA/D variants.

Validates the approach through comprehensive experiments.

Abstract

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.