A Hybrid Algorithm Based on Invasive Weed Optimization Algorithm and Grey Wolf Optimization Algorithm

W. Qasim, B. Mitras
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Abstract

In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the orig.
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基于入侵杂草优化算法和灰狼优化算法的混合算法
本文首先研究了两种算法,认为这是一种混合算法。它是代表入侵杂草优化的算法。该算法是一种随机数值算法,第二种算法代表灰狼优化。该算法是群智能在智能优化中的一种算法。入侵杂草优化算法受到自然的启发,因为杂草具有殖民行为,由Mehrabian和Lucas于2006年提出。入侵杂草由于其适应性而对栽培植物构成严重威胁,并对整个种植过程构成威胁。这些杂草的行为已经被研究并应用于入侵杂草算法中。灰狼算法被认为是一种群体智能算法,已被用于达到目标并获得最佳解。该算法由SeyedaliMirijalili于2014年设计,利用中队的智能是为了避免陷入局部解决方案,因此之前的算法GWO和IWO之间的新杂交过程,我们将象征新算法IWOGWO。将所提出的混合算法与原始算法进行了比较。
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