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Adaptive cooperative co-evolution for large scale global optimization

机译:大规模大规模优化的自适应合作协同进化

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Large-scale global optimization (LSGO) is a very important and challenging task in optimization domain, which is embedded in many scientific and engineering applications. Previously, the cooperative co-evolution (CC) is a usual and effective choice for LSGO problems. In this paper, aim at more fully exploring the flexibility and potential of CC strategy, an adaptive CC (ACC) is designed to handle LSGO problems. The advantages of ACC compared with the classical CC strategies are experimentally verified on a set of widely used large scale function optimization problems.
机译:大规模全局优化(LSGO)在优化领域是一项非常重要且具有挑战性的任务,它已嵌入许多科学和工程应用程序中。以前,合作共进化(CC)是解决LSGO问题的常用且有效的选择。在本文中,为了更充分地探索CC策略的灵活性和潜力,设计了一种自适应CC(ACC)来处理LSGO问题。在一系列广泛使用的大规模函数优化问题上,实验证明了ACC与经典CC策略相比的优势。

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