R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm
arxiv(2024)
摘要
Choosing an appropriate optimization algorithm is essential to achieving
success in optimization challenges. Here we present a new evolutionary
algorithm structure that utilizes a reinforcement learning-based agent aimed at
addressing these issues. The agent employs a double deep q-network to choose a
specific evolutionary operator based on feedback it receives from the
environment during optimization. The algorithm's structure contains five
single-objective evolutionary algorithm operators. This single-objective
structure is transformed into a multi-objective one using the R2 indicator.
This indicator serves two purposes within our structure: first, it renders the
algorithm multi-objective, and second, provides a means to evaluate each
algorithm's performance in each generation to facilitate constructing the
reinforcement learning-based reward function. The proposed R2-reinforcement
learning multi-objective evolutionary algorithm (R2-RLMOEA) is compared with
six other multi-objective algorithms that are based on R2 indicators. These six
algorithms include the operators used in R2-RLMOEA as well as an R2
indicator-based algorithm that randomly selects operators during optimization.
We benchmark performance using the CEC09 functions, with performance measured
by inverted generational distance and spacing. The R2-RLMOEA algorithm
outperforms all other algorithms with strong statistical significance (p<0.001)
when compared with the average spacing metric across all ten benchmarks.
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