A Hybrid Particle Swarm Optimization for Numerical Optimization

机译：用于数值优化的混合粒子群算法

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Particle Swarm Optimization (PSO) has shown its good performance on numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in velocity. This paper presents a hybrid PSO for numerical optimization, namely HPSO, which employs opposition-based learning (OBL) and a modified velocity model. The OBL provides more chances to find solutions more closely to the global optimum. And the modified velocity model guarantees a non-zero velocity to help trapped particles jump out local minima. Experimental results on 6 benchmark functions show that the HPSO outperforms the standard PSO and opposition-based PSO in all test cases.

机译：粒子群优化（PSO）在数值函数问题上显示出了良好的性能。但是，在某些多峰函数上，由于速度的快速下降，PSO容易过早收敛。本文提出了一种用于数值优化的混合PSO，即HPSO，它采用了基于对立的学习（OBL）和改进的速度模型。 OBL提供了更多机会来寻找更接近全局最优值的解决方案。改进的速度模型可确保非零速度，以帮助捕获的粒子跳出局部最小值。在6个基准功能上的实验结果表明，在所有测试用例中，HPSO均优于标准PSO和基于对立的PSO。

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来源
《The Second International Conference on Business Intelligence and Financial Engineering(BIFE 2009)（第二届商务智能与金融工程国际会议）》|2009年|92-96|共5页
会议地点 Beijing(CN);Beijing(CN)
作者
Zhengang Ning; Liyan Ma; Zhenping Li; Wenjian Xing;
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作者单位

School of Information Electronic Engineering, Hebci University of Engineering, Handan 056038, China;

School of Information Electronic Engineering, Hebci University of Engineering, Handan 056038, China;

School of Information Electronic Engineering, Hebci University of Engineering, Handan 056038, China;

The Modern Education Technology Center, Hebei University of Engineering, Handan 056038, China;

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原文格式 PDF
正文语种 eng
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关键词
Particle swarm optimization; function optimization; numerical optimization;

机译：粒子群优化；功能优化；数值优化;

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A Hybrid Particle Swarm Optimization for Numerical Optimization

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