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首页> 外文期刊>Arabian Journal for Science and Engineering >Hybrid Nelder–Mead Algorithm and Dragonfly Algorithm for Function Optimization and the Training of a Multilayer Perceptron
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Hybrid Nelder–Mead Algorithm and Dragonfly Algorithm for Function Optimization and the Training of a Multilayer Perceptron

机译：Nelder-Mead和Dragonfly混合算法用于功能优化和多层感知器的训练

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摘要

Dragonfly algorithm (DA) is a new optimization technique based on swarm intelligence. DA simulates the static and dynamic swarming behaviors of dragonflies in nature. The search pattern of DA consists of two essential phases: exploration and exploitation that are inspired by the survival rule of dragonflies in navigating, searching for food and fleeing enemies when dynamically or statistically swarming. This method is straightforward to implement and is efficient in solving real-world problems. However, an excessive number of social interactions in DA may result in low solution accuracy, easy stagnation at local optima andan imbalance between exploration and exploitation. To overcome these deficiencies, an improved Nelder-Mead algorithm is added to the conventional DA (INMDA) to strengthen its local explorative capability and avoid the possibility of falling into local optima. Simulation experiments were conducted on several well-known benchmark functions with different dimensions. In addition, the three classic classification problems are utilized to benchmark the performance of the proposed algorithm in training a multilayer perceptron. The experimental results and statistical significance show that the performance of the proposed INMDA is superior to that of the other algorithms.
机译：蜻蜓算法（DA）是基于群体智能的一种新的优化技术。 DA模拟了自然界中蜻蜓的静态和动态群集行为。 DA的搜索模式包括两个基本阶段：探索和开发，其灵感来自蜻蜓在导航，寻找食物和动态或统计蜂群逃离时的生存规则。该方法易于实现，并且在解决实际问题中非常有效。但是，DA中过多的社会互动可能导致解决方案准确性低，局部最优情况下容易停滞以及勘探与开发之间的不平衡。为了克服这些不足，将改进的Nelder-Mead算法添加到常规DA（INMDA）中，以增强其局部探索能力，并避免陷入局部最优的可能性。模拟实验是在几个不同维度的著名基准函数上进行的。此外，利用三个经典分类问题来对所提出的算法在训练多层感知器中的性能进行基准测试。实验结果和统计意义表明，所提出的INMDA的性能优于其他算法。

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