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首页> 外文期刊>Advances in Mechanical Engineering >Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization:
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Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization:

机译：具有基于实用程序的自适应分组的协作差分进化框架，用于大规模优化：

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Decomposing the large-scale problem into small-scale subproblems and optimizing them cooperatively are critical steps for solving large-scale optimization problem. This article proposes a cooperative differential evolution with utility-based adaptive grouping. The problem decomposition is adaptively executed by the two mechanisms of circular sliding controller and relation matrix, which consider the variable interactions on the basis of the short-term and long-term utilities, respectively. The circular sliding controller provides baselines for the subproblem optimizer. The size of the sliding window and the sliding speed in the controller are adjusted adaptively so that the variables with higher activeness can be optimized extensively. The relation matrix–based grouping strategy enables interacted variables to be grouped into the same subproblem with higher probabilities. The novelty is that decomposition is conducted as the optimization process without extra computational burden. For subproblem optimizat...
机译：将大规模问题分解为小规模子问题，并对其进行优化是解决大规模优化问题的关键步骤。本文提出了基于效用的自适应分组的协作差分进化。通过循环滑动控制器和关系矩阵这两种机制自适应地执行问题分解，这两种机制分别基于短期和长期效用考虑变量的相互作用。循环滑动控制器为子问题优化器提供了基线。自适应调整滑动窗口的大小和控制器中的滑动速度，从而可以广泛地优化具有较高活动性的变量。基于关系矩阵的分组策略使交互变量可以分组为具有较高概率的相同子问题。新颖之处在于，分解是作为优化过程进行的，而没有额外的计算负担。对于子问题的优化...

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