精英粒子群优化算法及其在机器人路径规划中的应用

颜雪松1; 胡成玉1; 姚宏1; 伍庆华2

doi:10.3788/OPE.20132112.3160

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精英粒子群优化算法及其在机器人路径规划中的应用

微纳技术与精密机械 | 更新时间：2020-08-12

- 精英粒子群优化算法及其在机器人路径规划中的应用
- Elite particle swarm optimization algorithm and its application in robot path planning
- 光学精密工程 2013年21卷第12期页码：3160-3168
- 作者机构：
  
  1. 中国地质大学计算机学院,湖北武汉,430074
  2. 武汉工程大学计算机科学与工程学院,湖北武汉,430073
- 作者简介：
- 基金信息：
  
  动态多策略差分演化算法及其在无线传感器网络能量分配优化中的应用()
- DOI：10.3788/OPE.20132112.3160
  中图分类号： TP242.6;TP301.6
- 收稿日期：2013-06-21，
  
  修回日期：2013-08-24，
  
  网络出版日期：2013-12-25，
  
  纸质出版日期：2013-12-25
- 稿件说明：
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颜雪松, 胡成玉, 姚宏, 伍庆华. 精英粒子群优化算法及其在机器人路径规划中的应用[J]. 光学精密工程, 2013,21(12): 3160-3168

YA Xue-Song, HU Cheng-Yu, TAO Hong, WU Qing-Hua. Elite particle swarm optimization algorithm and its application in robot path planning[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3160-3168
颜雪松, 胡成玉, 姚宏, 伍庆华. 精英粒子群优化算法及其在机器人路径规划中的应用[J]. 光学精密工程, 2013,21(12): 3160-3168 DOI： 10.3788/OPE.20132112.3160.

YA Xue-Song, HU Cheng-Yu, TAO Hong, WU Qing-Hua. Elite particle swarm optimization algorithm and its application in robot path planning[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3160-3168 DOI： 10.3788/OPE.20132112.3160.

摘要

针对标准粒子群优化(PSO)算法容易陷入局部最优的缺点，提出了一种基于标准PSO算法的新算法。该算法通过引入新的更新函数和精英选择策略，可在保持较高收敛速度的同时，降低陷入局部最优的可能性。与标准PSO算法相比较，不仅扩大了搜索空间，并且复杂度也不高。研究结果证明该算法更容易引导，而且具有更高效的全局搜索能力，展示了较高的效率和鲁棒性。将该算法用于解决机器人路径规划问题并进行了仿真实验，结果显示，与传统的方法相比，新算法在机器人路径规划问题上能获得更加准确的路径，而且计算时间可以缩短15%，比其他算法更有效。

Abstract

To overcome the shortcomings of standard Particle Swarm Optimization (PSO) algorithm that is easy to trapp into a local optimum

the improved PSO was proposed based on the standard PSO. By inducing the renewal functions and a best selecting strategy

the improved algorithm could keep the fast convergence speed and reduce the possibility of trapping into a local optimum. As compared the standard PSO

the improved algorithm not only enlarges the searing space but also shows lower complexity. Obtained results prove that the algorithm is easy to be induced and has a higher global searching ability in high efficiency and better robustness. The improved algorithm was applied to solving the robot path planning problem. The simulation experiments show that the improved algorithm can get more accurate path in the robot path planning and its calculation time can reduced by 15% as compared with that of traditional methods. These results prove the feasibility and efficiency of the improved algorithm.

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references

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