| 基于仿生学改进的粒子群算法 |
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| 引用本文： | 那日苏,李 强,乌力吉. 基于仿生学改进的粒子群算法[J]. 计算机工程与应用, 2014, 50(6): 61-63 |
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| 作者姓名： | 那日苏 李 强 乌力吉 |
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| 作者单位： | 1.内蒙古工业大学 机械学院，呼和浩特 0100512.北方工业大学 机电工程学院，北京 1001443.内蒙古工业大学 理学院，呼和浩特 010051 |
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| 基金项目： | 国家科技支撑项目计划（No.2011BAG03803）. |
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| 摘 要： | 针对标准粒子群算法收敛速度慢和易陷入局部最优的局限性，提出了一种基于仿生学改进的粒子群算法。即通过在标准粒子群公式中加入负梯度项，使算法更加符合鸟群觅食的实际规律，同时使算法的全局和局部搜索能力得到了平衡。仿真对比结果表明，改进的粒子群算法减小了陷入局部极值的可能性，能够提高最优解的精度和优化效率。
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| 关 键 词： | 粒子群 负梯度 仿生学 |
Particle Swarm Optimization based on bionics |
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| NA Risu,LI Qiang,WU Liji. Particle Swarm Optimization based on bionics[J]. Computer Engineering and Applications, 2014, 50(6): 61-63 |
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| Authors: | NA Risu LI Qiang WU Liji |
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| Affiliation: | 1.College of Mechanics, Inner Mongolia University of Technology, Hohhot 010051, China2.College of Mechanical and Electrical Engineering, North China University of Technology, Beijing 100144, China3.College of Science, Inner Mongolia University of Technology, Hohhot 010051, China |
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| Abstract: | The classic Particle Swarm Optimization has some deficiencies, such as falling in the local optimal region, slow convergence velocity, and so on. Aimed at these disadvantages an improved PSO algorithm is proposed. By employing the information about negative gradient to the standard particle swarm algorithm formula, an improved PSO algorithm can make the equilibrium more closed to the real rules of birds swarm’s foraging. At the same time, the global and local search ability of algorithm is balanced. Simulation results show that, an improved PSO algorithm reduces the chances of getting into the local extremum. At the same time, it can improve the solution accuracy of optimal solution and optimizing efficiency. |
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| Keywords: | Particle Swarm Optimization（PSO） negative gradient bionics |
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