动态调整进化方向与策略的果蝇优化算法
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江西理工大学信息工程学院

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TP301.6

基金项目:

国家自然科学基金(61562037)、江西省教育厅科学技术研究项目(GJJ180442)、江西省研究生创新专项基金项目(YC2018-S330)


Fruit Fly Optimization Algorithm with Dynamic Adjustment of Evolutionary Direction and Strategy
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School of Information Engineering, Jiangxi University of Science and Technology

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The National Natural Science Foundation of China (61562037)、Science and Technology Project of the Education Department of Jiangxi Province(GJJ180442)、Innovation Fund Designated for Graduate Students of Jiangxi Province(YC2018-S330)

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    摘要:

    针对标准果蝇优化算法(FOA)收敛速度慢、容易陷入局部最优和寻优精度低等缺陷,提出了动态调整进化方向与策略的果蝇优化算法(FOADAEDS)。首先,种群初始位置由佳点集理论选取;其次,根据种群进化信息动态调整进化指导方向和搜索步长;最后,当算法陷入早熟时,改变搜索策略以跳出局部最优。对6个经典测试函数进行仿真运算,结果表明,本文提出的改进算法相比标准果蝇优化算法和其他几种改进算法,有较好的寻优精度和收敛速度。

    Abstract:

    For the demerits of Fruit Fly Optimization Algorithm(FOA), such as slow convergence rate, easily relapsing into local optimum and low convergence precision, an improved Fruit Fly Optimization Algorithm with Dynamic Adjustment of Evolutionary Direction and Strategy (FOADAEDS) is presented. Firstly, the selection of the initial position of the population was improved using the optimal point set method. Secondly, evolutionary direction and search step are dynamically adjusted according to the population evolutionary information. Finally, when the algorithm falls into premature, the search strategy is changed to jump out of local optimum. Experiments were conducted on the six kinds of classical test functions for operation simulation.And the results show that the proposed algorithm is better than basic FOA and its several improved algorithms in convergence precision and convergence rate.

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引用本文

张水平,高栋,李殷俊,等. 动态调整进化方向与策略的果蝇优化算法[J]. 科学技术与工程, 2019, 19(21): 206-212.
ZHANG Shui-ping, GAO Dong, LI Yin-jun, et al. Fruit Fly Optimization Algorithm with Dynamic Adjustment of Evolutionary Direction and Strategy[J]. Science Technology and Engineering,2019,19(21):206-212.

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  • 收稿日期:2019-01-28
  • 最后修改日期:2019-03-03
  • 录用日期:2019-04-01
  • 在线发布日期: 2019-08-08
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