求解大规模优化问题的改进麻雀搜索算法
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1.西安建筑科技大学资源工程学院,西安市智慧工业感知计算与决策重点实验室;2.西安建筑科技大学管理学院,西安市智慧工业感知计算与决策重点实验室

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TP273

基金项目:

国家自然科学基金面上资助项目:金属露天矿无人驾驶多工序多目标协同智能调度方法研究(52074205);陕西省自然科学基金杰青资助项目:时空路况下金属露天矿无人驾驶多车协同智能调度集成建模(2020JC-44)


An Improved Sparrow Search Algorithm for Solving Large-scale Optimization Problems
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Affiliation:

1.School of Resource Engineering, Xi’an University of Architecture and Technology,Xi'2.'3.an Key Laboratory of Intelligent Industry Perception Computing and Decision Making;4.School of management,Xi’an University of Architecture and Technology,Xi'

Fund Project:

General supported project of National Natural Science Foundation of China: Research on unmanned multi process and multi-objective collaborative intelligent scheduling method of metal open pit mine (52074205); Project supported by outstanding youth of Natural Science Foundation of Shaanxi Province: Integrated Modeling of driverless multi vehicle Cooperative Intelligent Dispatching of metal open pit mine under space-time road conditions (2020jc-44)

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    摘要:

    针对麻雀搜索算法在求解大规模优化问题时存在收敛速度慢、寻优精度低和易陷入局部极值的缺点,提出了一种基于精英反向学习策略的萤火虫麻雀搜索算法(ELFASSA)。首先通过反向学习策略初始化种群,为全局寻优奠定基础;其次,利用萤火虫扰动策略提高算法跳出局部最优的能力并加速收敛;最后,在麻雀位置更新后引入精英反向学习策略以获取精英解及动态边界,使精英反向解可以定位在狭窄的搜索空间中,有利于算法收敛。通过选取10个高维标准测试函数进行仿真实验,将其与麻雀搜索算法(SSA)及四种先进的改进算法进行性能对比,并与三种单一策略改进的麻雀搜索算法进行改进策略的有效性分析,仿真结果表明,ELFASSA算法在收敛速度和求解精度等方面明显优于其他对比算法。

    Abstract:

    Aiming at the disadvantages of slow convergence, low optimization accuracy and easy to fall into local extremum in sparrow search algorithm for solving large-scale optimization problems, a firefly sparrow search algorithm based on elite reverse learning strategy (elfassa) is proposed. Firstly, the population is initialized by reverse learning strategy to lay the foundation for global optimization; Secondly, the firefly perturbation strategy is used to improve the ability of the algorithm to jump out of the local optimum and accelerate the convergence; Finally, after the sparrow position is updated, the elite reverse learning strategy is introduced to obtain the elite solution and dynamic boundary, so that the elite reverse solution can be located in the narrow search space, which is conducive to the convergence of the algorithm. By selecting 10 high-dimensional standard test functions for simulation experiments, its performance is compared with sparrow search algorithm (SSA) and four advanced improved algorithms, and the effectiveness of the improved strategy is analyzed with three single strategy improved sparrow search algorithms. The simulation results show that elfassa algorithm is obviously superior to other comparison algorithms in convergence speed and solution accuracy.

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  • 收稿日期:2021-11-29
  • 最后修改日期:2022-03-09
  • 录用日期:2022-03-15
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