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A Modified Invasive Weed Optimization with Crossover Operation

机译:具有交叉操作的改进的侵入性杂草优化

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Invasive weed optimization, which is inspired from the invasive habits of growth of weeds in nature, is a populationbased intelligence algorithm. In this paper, we present invasive weed optimization with crossover operation combining the idea of the invasive weed with concepts from evolutionary algorithms. By applying the crossover operation in invasive weed optimization, it not only discourages premature convergence to local optimum but also explores and exploits the promising regions in the search space effectively. This modified algorithm is tested and compared with the standard invasive weed optimization and PSO. The comparative experiments have been conducted on benchmark test functions; invasive weed optimization with crossover operation is able to obtain the result superior to the standard invasive weed optimization and PSO.
机译:入侵杂草优化是基于种群的智能算法,它是从自然界中杂草生长的侵入性习惯中获得启发的。在本文中,我们提出了一种具有交叉操作的侵入性杂草优化方法,将侵入性杂草的思想与进化算法的概念相结合。通过将交叉运算应用于侵入性杂草优化中,它不仅不鼓励过早收敛到局部最优,而且可以有效地探索和利用搜索空间中有希望的区域。测试了该改进算法,并将其与标准侵入性杂草优化和PSO进行了比较。对比实验已经在基准测试功能上进行;具有交叉操作的侵入性杂草优化能够获得优于标准侵入性杂草优化和PSO的结果。

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