Distributed Evolution Strategies With Multi-Level Learning for Large-Scale Black-Box Optimization

In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO). Here we propose to parallelize the well-established covariance matrix adaptation evolution strategy (CMA-ES) and in particular its one lates...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2024-11, Vol.35 (11), p.2087-2101
Main Authors: Duan, Qiqi, Shao, Chang, Zhou, Guochen, Zhang, Minghan, Zhao, Qi, Shi, Yuhui
Format: Article
Language:English
Subjects:
Black-box optimization (BBO)
Closed box
Complexity theory
Computational modeling
Distributed computing
distributed optimization
evolution strategies (ESs)
large-scale optimization (LSO)
Optimization
Parallel processing
parallelism
Runtime
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Summary:In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO). Here we propose to parallelize the well-established covariance matrix adaptation evolution strategy (CMA-ES) and in particular its one latest LSO variant called limited-memory CMA-ES (LM-CMA). To achieve efficiency while approximating its powerful invariance property, we present a multilevel learning-based meta-framework for distributed LM-CMA. Owing to its hierarchically organized structure, Meta-ES is well-suited to implement our distributed meta-framework, wherein the outer-ES controls strategy parameters while all parallel inner-ESs run the serial LM-CMA with different settings. For the distribution mean update of the outer-ES, both the elitist and multi-recombination strategy are used in parallel to avoid stagnation and regression, respectively. To exploit spatiotemporal information, the global step-size adaptation combines Meta-ES with the parallel cumulative step-size adaptation. After each isolation time, our meta-framework employs both the structure and parameter learning strategy to combine aligned evolution paths for CMA reconstruction. Experiments on a set of large-scale benchmarking functions with memory-intensive evaluations, arguably reflecting many data-driven optimization problems, validate the benefits (e.g., effectiveness w.r.t. solution quality, and adaptability w.r.t. second-order learning) and costs of our meta-framework.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2024.3437688