具有入侵杂草策略的花朵授粉算法
发布时间：2018-10-14 15:00
【摘要】：针对花朵授粉算法易陷入局部极值、收敛速度慢的不足,提出一种具有入侵杂草策略的花朵授粉算法。该算法通过入侵杂草的繁殖、空间扩散和竞争策略,动态生成种群,增加种群的多样性和有效性,使算法能有效地避免陷入局部最优,增强全局寻优能力,提高收敛速度。通过8个CEC2005benchmark测试函数进行测试比较,仿真结果表明,改进算法的全局寻优能力明显优于基本的花朵授粉算法、差分进化算法和蝙蝠算法,其收敛精度、收敛速度、鲁棒性均较对比算法有较大提高。
[Abstract]:A flower pollination algorithm with invasive weed strategy is proposed to solve the problem that flower pollination algorithm is easy to fall into local extremum and converge slowly. The algorithm can dynamically generate population by invading weed propagation, spatial diffusion and competition strategy to increase the diversity and effectiveness of the population, so that the algorithm can effectively avoid falling into local optimum, enhance the ability of global optimization, and improve the convergence speed. Compared with eight CEC2005benchmark test functions, the simulation results show that the global optimization ability of the improved algorithm is obviously superior to that of the basic flower pollination algorithm, differential evolution algorithm and bat algorithm, and its convergence accuracy and convergence speed are better than those of the basic flower pollination algorithm, differential evolution algorithm and bat algorithm. The robustness is better than the contrast algorithm.
【作者单位】： 河池学院计算机与信息工程学院;江西财经大学信息管理学院;
【基金】：国家自然科学基金(61165015,61562032) 河池学院科研项目(XJ2015QN003)
【分类号】：TP18
，
本文编号：2270821
[Abstract]:A flower pollination algorithm with invasive weed strategy is proposed to solve the problem that flower pollination algorithm is easy to fall into local extremum and converge slowly. The algorithm can dynamically generate population by invading weed propagation, spatial diffusion and competition strategy to increase the diversity and effectiveness of the population, so that the algorithm can effectively avoid falling into local optimum, enhance the ability of global optimization, and improve the convergence speed. Compared with eight CEC2005benchmark test functions, the simulation results show that the global optimization ability of the improved algorithm is obviously superior to that of the basic flower pollination algorithm, differential evolution algorithm and bat algorithm, and its convergence accuracy and convergence speed are better than those of the basic flower pollination algorithm, differential evolution algorithm and bat algorithm. The robustness is better than the contrast algorithm.
【作者单位】： 河池学院计算机与信息工程学院;江西财经大学信息管理学院;
【基金】：国家自然科学基金(61165015,61562032) 河池学院科研项目(XJ2015QN003)
【分类号】：TP18
，
本文编号：2270821
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