Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study
Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study
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DOI：
10.1109/cec48606.2020.9185493
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
K. Nishihara;Masaya Nakata
中科院分区：
文献类型：
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作者：
K. Nishihara;Masaya Nakata
提出了一种基于竞争-自适应的元启发式算法优化框架。我们提出的方法，称为CAT，是受经济学中的均衡理论的启发，该理论解释了竞争对手最终收敛到均衡状态，例如在产品价格方面。详细地说，我们的建议运行具有不同算法配置的多个优化器，例如突变变体。然后，自适应地调整下级优化器的配置，使得它们能够导出上级优化器已经导出的好的解。这旨在通过以下技术优势来提高性能，即使健身评估的数量有限。CAT初步验证了调整后的算法配置的搜索能力，然后通过使用多个优化器构建集成优化器。作为一个案例研究，本文应用CAT来调整差分进化算法（DE）。实验结果表明，我们的建议优于标准DE和替代方法，即jDE，它适应遗传算子的超参数。
This paper proposes a competitive-adaptive algorithm tuning framework for meta-heuristic algorithms. Our proposed method, called CAT, is inspired by the Equilibrium Theory in economics, which explains competitors eventually converge to an equilibrium status, e.g. in terms of the price of products. In detail, our proposal runs multiple optimizers with different algorithmic configurations, e.g. mutation variants. Then, the configurations of inferior optimizers are adaptively tuned so that they can derive good solutions that superior ones have derived. This intends to boost the performance even with a limited number of fitness evaluations, by the following technical advantage. The CAT preliminarily validates a search capacity of tuned algorithmic configurations and then constructs an ensemble optimizer by utilizing multiple optimizers. As a case study, this paper applies the CAT to tune the differential evolution algorithms (DEs). Experimental results show that our proposal outperforms the standard DE and an alternative approach i.e. jDE, which adapts hyper-parameters of genetic operators.