计算机科学 ›› 2016, Vol. 43 ›› Issue (12): 248-254.doi: 10.11896/j.issn.1002-137X.2016.12.045

• 智能优化 • 上一篇    下一篇

一种均衡各速度项系数的多目标粒子群优化算法

耿焕同,赵亚光,陈哲,李辉健   

  1. 南京信息工程大学计算机与软件学院 南京210044,南京信息工程大学计算机与软件学院 南京210044,南京信息工程大学计算机与软件学院 南京210044,南京信息工程大学计算机与软件学院 南京210044
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(61403206),江苏省自然科学基金(BK20151458)资助

Multi-objective Particle Swarm Optimization Algorithm with Balancing Each Speed Coefficient

GENG Huan-tong, ZHAO Ya-guang, CHEN Zhe and LI Hui-jian   

  • Online:2018-12-01 Published:2018-12-01

摘要: 粒子群优化算法已成为求解多目标优化问题的有效方法之一,而速度更新公式中的惯性、局部和全局3个速度项的系数的动态合理设置是算法优化效率的关键问题。为解决现有算法仅单独设置各速度项系数导致优化效率不高的问题,提出了一种均衡各速度项系数的多目标粒子群优化算法。该方法旨在通过粒子的局部最优和全局最优的信息来引导种群的进化方向,动态调整每一个粒子速度项系数来均衡惯性、局部和全局3个速度项在搜索中的作用,从而更为准确地刻画算法的搜索能力和搜索精度,更好地平衡算法的探究和探索能力,进一步提高粒子群优化算法解决复杂多目标优化问题的效率。在7个标准测试函数上进行实验,并与5种经典的进化算法进行对比,结果表明新算法在综合指标IGD以及多样性评估指标Δ评分上具有更好的收敛速度和分布性,验证了新算法的有效性。

关键词: 粒子群优化算法,均衡,速度项系数,自适应,多目标优化

Abstract: PSO has become one of the effective methods for solving multi-objective optimization problems,and the key of PSO is the proper settings of the inertial,local and global velocity coefficients.To solve the problem,separating settings for each speed coefficient in existing algorithm with ignoring potential relevancies,an improved multi-objective particle optimization for balancing each formula element was proposed.For the purpose of guiding the evolutionary particle swarm in a potential global optimum,our algorithm can dynamically adjust the speed of each particle coefficients to balance inertia,local and global effects of three speed items during the searching process.Thus the searching capability and accuracy of the new algorithm is more accurate.Meanwhile,our algorithm can not only balance the capacity of exploitation and exploration,but also improve the efficiency in solving complex multi-objective optimization problem.The experimental results indicate that the new algorithm outperforms other 5 classical evolutionary algorithms in terms of convergence speed and distribution on 7 multi-objective benchmark functions.

Key words: Particle swarm optimization,Balance,Speed coefficient,Adaptive,Multi-objective optimization

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