Non-swarm intelligence search algorithm based on the foraging behaviors of fruit flies
Non-swarm intelligence search algorithm based on the foraging behaviors of fruit flies
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DOI：
10.1109/cec.2016.7743968
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
K. Ohnishi;A. Fujiwara;M. Köppen
中科院分区：
文献类型：
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作者：
K. Ohnishi;A. Fujiwara;M. Köppen
在本研究中，我们研究了果蝇(果蝇)的觅食行为，它不同于群居昆虫，独立和自主地觅食。此外，我们还提出了一种新的基于种群的、模仿果蝇觅食行为的非群体智能搜索算法。我们发现，果蝇在食物来源地停留的时间服从幂定律。由于果蝇的行为符合幂分布的无标度特性，可以称之为无标度行为，用该算法对果蝇的行为进行建模。通过对28个带有两个实数参数的测试函数的测试，我们考察了无尺度行为对搜索性能的影响。结果表明，无标度特性关系到勘探与开采之间的平衡，并影响搜索性能。此外，用于固定无标度属性的特定参数值对于在此考虑的大多数测试函数产生更好的搜索性能。本文并未给出解决静态优化问题的搜索算法，而是引入了个体行为的一种新的时态性质，即无标度性质，以期为搜索算法的研究提供一种新的思路。
In the present study, we examine the foraging behavior of the fruit fly (Drosophila), which unlike social insects, independently and autonomously forages. Moreover, we propose a new population-based, non-swarm intelligence search algorithm that mimics the foraging behaviors of fruit flies. We find that the time that a fruit fly stays in a food source region follows a power-law. A power-law distribution satisfies the scale-free property, so the observed behaviors of fruit flies can be called scale-free behaviors, which are modeled by the proposed algorithm. We examine the effect of scale-free behaviors on search performance by applying the proposed algorithm to 28 test functions with two real parameters. The results reveal that the scale-free property is related to a balance between exploration and exploitation and affects the search performance. Moreover, particular parameter values for fixing the scale-free property yield a better search performance for most of the test functions considered herein. The present paper does not provide a competent search algorithm for solving static optimization problems, but rather introduces a novel temporal property of individual behaviors, i.e., a scale-free property, which is expected to provide a new way to think about search algorithms.