An Improved Artificial Weed Colony for Continuous Optimization

A. Rahimi, Milad Ahangaran, P. Ramezani, Tarlan Kashkooli
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引用次数: 3

Abstract

In this paper, after a literature review, studies will be concentrated on standard deviation of invasive weedoptimization's normal distribution function which is used for distributing seeds of each weed over the search space. Although invasive weed optimization is a great algorithm to solve real world practical optimization problems but there is a serious drawback in distributing the seeds over the search space. A new concept will be presented to distribute seeds of each weed over the search space which increases the robustness and effectiveness of algorithm, and therefore leads to an improved invasive weed optimization. Simulation on a set of unconstrained benchmark functions reveals the superiority of the proposed algorithm in quick convergence and finding better solutions compared to the original invasive weed optimization.
一种改进的连续优化人工杂草群
在文献综述之后,本文将重点研究入侵杂草优化的正态分布函数的标准差,该函数用于在搜索空间中分布每种杂草的种子。虽然入侵杂草优化是解决现实世界实际优化问题的一个很好的算法,但在搜索空间中分布种子有一个严重的缺点。提出了在搜索空间中分布每种杂草种子的新概念,提高了算法的鲁棒性和有效性,从而改进了入侵杂草优化算法。在一组无约束基准函数上的仿真表明,与原有的入侵杂草优化算法相比,本文算法具有快速收敛和找到更好解的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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