Minimax optimal designs via particle swarm optimization methods
Minimax optimal designs via particle swarm optimization methods
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DOI：
10.1007/s11222-014-9466-0
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发表时间：
2015-09-01
影响因子：
2.2
通讯作者：
Wong, Weng Kee
中科院分区：
文献类型：
--
作者：
Chen, Ray-Bing;Chang, Shin-Perng;Wong, Weng Kee
粒子群优化（PSO）技术被广泛应用于应用领域，以解决具有挑战性的优化问题，但他们似乎没有在主流统计应用的影响。PSO方法很受欢迎，因为它们易于实现和使用，并且似乎越来越能够解决复杂的问题，而无需对目标函数进行任何假设。我们修改PSO技术，找到极大极小最优设计，这是出了名的挑战，即使是线性模型，并表明PSO方法可以很容易地产生各种极大极小最优设计的一种新颖而有趣的方式，包括适应算法生成标准化的极大极小最优设计。
Particle swarm optimization (PSO) techniques are widely used in applied fields to solve challenging optimization problems but they do not seem to have made an impact in mainstream statistical applications hitherto. PSO methods are popular because they are easy to implement and use, and seem increasingly capable of solving complicated problems without requiring any assumption on the objective function to be optimized. We modify PSO techniques to find minimax optimal designs, which have been notoriously challenging to find to date even for linear models, and show that the PSO methods can readily generate a variety of minimax optimal designs in a novel and interesting way, including adapting the algorithm to generate standardized maximin optimal designs.