A novel numerical optimization algorithm inspired from weed colonization
A novel numerical optimization algorithm inspired from weed colonization
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DOI：
10.1016/j.ecoinf.2006.07.003
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发表时间：
2006-12-01
影响因子：
5.1
通讯作者：
Lucas, C.
中科院分区：
文献类型：
--
作者：
Mehrabian, A. R.;Lucas, C.
本文介绍了一种新的数值随机优化算法的启发殖民杂草。杂草是一种植物，其旺盛的侵入性生长习性对理想的栽培植物构成严重威胁，使其成为农业的威胁。杂草已被证明是非常强大的和适应环境的变化。因此，捕获它们的属性将导致强大的优化算法。本文提出了一种简单有效的杂草入侵优化算法（伊沃），以模拟杂草入侵的鲁棒性、适应性和随机性。通过一组已知全局极小值和局部极小值的多维基准函数，对伊沃的可行性、效率和有效性进行了详细的测试。报告的结果进行了比较，与其他最近的进化为基础的算法：遗传算法，模因算法，粒子群优化，和洗牌蛙跳。结果还比较了不同版本的模拟退火-一个通用的概率元算法的全局优化问题-这是单纯形模拟退火，直接搜索模拟退火。此外，伊沃被用来寻找一个工程问题的解决方案，这是一个鲁棒控制器的优化和调整。实验结果表明，伊沃的结果优于其他方法的结果。总之，伊沃的性能对于所有测试功能都具有合理的性能。(c)2006 Elsevier B. V.保留所有权利。
This paper introduces a novel numerical stochastic optimization algorithm inspired from colonizing weeds. Weeds are plants whose vigorous, invasive habits of growth pose a serious threat to desirable, cultivated plants making them a threat for agriculture. Weeds have shown to be very robust and adaptive to change in environment. Thus, capturing their properties would lead to a powerful optimization algorithm. It is tried to mimic robustness, adaptation and randomness of colonizing weeds in a simple but effective optimizing algorithm designated as Invasive Weed Optimization (IWO). The feasibility, the efficiency and the effectiveness of IWO are tested in details through a set of benchmark multi-dimensional functions, of which global and local minima are known. The reported results are compared with other recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, and shuffled frog leaping. The results are also compared with different versions of simulated annealing - a generic probabilistic meta-algorithm for the global optimization problem - which are simplex simulated annealing, and direct search simulated annealing. Additionally, IWO is employed for finding a solution for an engineering problem, which is optimization and tuning of a robust controller. The experimental results suggest that results from IWO are better than results from other methods. In conclusion, the performance of IWO has a reasonable performance for all the test functions. (c) 2006 Elsevier B.V. All rights reserved.