基于多智能体混沌鸟群算法的机构优化

吴冬梅, 郝凤鸣, 蒋国平

吴冬梅, 郝凤鸣, 蒋国平. 基于多智能体混沌鸟群算法的机构优化[J]. 信息与控制, 2021, 50(4): 449-458. DOI: 10.13976/j.cnki.xk.2021.0381
引用本文: 吴冬梅, 郝凤鸣, 蒋国平. 基于多智能体混沌鸟群算法的机构优化[J]. 信息与控制, 2021, 50(4): 449-458. DOI: 10.13976/j.cnki.xk.2021.0381
WU Dongmei, HAO Fengming, JIANG Guoping. Mechanism Optimization Based on Multi-agent Chaos Bird Swarm Algorithm[J]. INFORMATION AND CONTROL, 2021, 50(4): 449-458. DOI: 10.13976/j.cnki.xk.2021.0381
Citation: WU Dongmei, HAO Fengming, JIANG Guoping. Mechanism Optimization Based on Multi-agent Chaos Bird Swarm Algorithm[J]. INFORMATION AND CONTROL, 2021, 50(4): 449-458. DOI: 10.13976/j.cnki.xk.2021.0381
吴冬梅, 郝凤鸣, 蒋国平. 基于多智能体混沌鸟群算法的机构优化[J]. 信息与控制, 2021, 50(4): 449-458. CSTR: 32166.14.xk.2021.0381
引用本文: 吴冬梅, 郝凤鸣, 蒋国平. 基于多智能体混沌鸟群算法的机构优化[J]. 信息与控制, 2021, 50(4): 449-458. CSTR: 32166.14.xk.2021.0381
WU Dongmei, HAO Fengming, JIANG Guoping. Mechanism Optimization Based on Multi-agent Chaos Bird Swarm Algorithm[J]. INFORMATION AND CONTROL, 2021, 50(4): 449-458. CSTR: 32166.14.xk.2021.0381
Citation: WU Dongmei, HAO Fengming, JIANG Guoping. Mechanism Optimization Based on Multi-agent Chaos Bird Swarm Algorithm[J]. INFORMATION AND CONTROL, 2021, 50(4): 449-458. CSTR: 32166.14.xk.2021.0381

基于多智能体混沌鸟群算法的机构优化

基金项目: 

国家自然科学基金面上项目 61873326

江苏省自然科学基金青年基金资助项目 BK20130873

南京邮电大学科研项目 NY220216

详细信息
    作者简介:

    吴冬梅(1983-), 女, 博士, 讲师, 硕士生导师.研究领域为智能优化算法及应用, 系统建模与仿真

    郝凤鸣(1996-), 女, 硕士生.研究领域为智能优化算法, 深度学习

    蒋国平(1966-), 男, 博士, 教授, 博士生导师.研究领域为复杂网络建模与控制, 混沌控制, 混沌信息处理

    通讯作者:

    吴冬梅, wudm@njupt.edu.cn

  • 中图分类号: TP18

Mechanism Optimization Based on Multi-agent Chaos Bird Swarm Algorithm

  • 摘要:

    平面四连杆机构是一种常见的传动机构,对机构参数进行优化设计是获得最佳动力性能的重要途径.为了解决这一机构参数优化问题,提出了一种多智能体混沌鸟群算法(multi-agent chaos bird swarm algorithm,MACBSA).该算法将多智能体系统中智能体的行动策略和混沌搜索机制引入鸟群算法的进化过程.多智能体的竞争与合作机制可以提高个体学习与信息交互的能力,增强群体内部的多样性和信息反馈;而混沌搜索则能够帮助算法跳出局部最优.最后,该算法在4个标准函数中进行了测试,并应用到四连杆机构参数优化问题,实验结果表明与其他7种算法相比,该算法在精度、收敛速度和鲁棒性等方面具有明显的优越性,更适合解决此类机构优化问题.

    Abstract:

    Planar four-bar linkage is a common transmission mechanism. The optimal design of mechanical parameters is an important way to achieve the best dynamic performance. In this study, a multi-agent chaos bird swarm algorithm (MACBSA) is proposed to deal with the mechanical parameters' optimization problem. The proposed algorithm combines the action policy of agents in a multi-agent system and a chaos search strategy with an evolutionary process in a bird swarm algorithm (BSA). The competition and cooperation scheme between agents improves the information interaction and learning ability of individuals. It also enhances the diversity and information feedback within the swarm. In addition, the chaos search helps the algorithm break away from the local optimum. Lastly, the proposed algorithm is tested on four benchmark functions and then applied to optimizing the design of the planar linkage mechanism. Simulation results reveal that the algorithm has more advantages over seven other algorithms in precision, convergence speed, and robustness. It is more suitable for solving such mechanism optimization problems.

  • 图  1   平面四连杆机构

    Figure  1.   Plane four-bar linkage

    图  2   机构摇杆的输出角

    Figure  2.   Output angle of rocker mechanism

    图  3   传动角极限位置

    Figure  3.   Limit position of transmission angle

    图  4   多智能体的格子结构

    Figure  4.   Lattice structure of multi-agent system

    图  5   μ∈[3, 4]时的混沌序列演化曲线

    Figure  5.   Chaos sequence evolution curve when μ∈[3, 4]

    图  6   多智能体混沌鸟群算法流程图

    Figure  6.   Flowchart of MACBSA

    图  7   基于不同算法的收敛曲线比较

    Figure  7.   Comparison of convergence curves by different algorithms

    图  8   收敛曲线

    Figure  8.   Convergence curves

    表  1   测试函数

    Table  1   Test functions

    函数名 表达式 维数 边界条件 理论最优值
    Sphere 20 [-100, 100] 0
    Rosenbrock 2 [-2.048,2.048] 0
    Griewank 20 [-600, 600] 0
    Rastrigin 20 [-5.12,5.12] 0
    下载: 导出CSV

    表  2   参数设置

    Table  2   Parameters setting

    参数 本文算法 文[11]算法 文[7]算法 混沌鸟群算法 自适应鸟群算法 BSA算法 PSO算法 ACO算法
    学习因子c1c2 1.5 参见式(26) 1.5 1.5 参见式(27) 1.5 1.496 18
    a1a2 1.0 1.0 1.0 1.0 1.0 1.0
    觅食概率P [0.8,1]
    随机数
    [0.8,1]
    随机数
    [0.8,1]
    随机数
    [0.8,1]
    随机数
    [0.8,1]
    随机数
    [0.8,1]
    随机数
    跟随系数FL [0.5,0.9]
    随机数
    [0.5,0.9]
    随机数
    [0.5,0.9]
    随机数
    [0.5,0.9]
    随机数
    [0.5,0.9]
    随机数
    [0.5,0.9]
    随机数
    飞行频率FQ 10 10 10 10 10 10
    种群规模M或多智能体结构Lsize×Lsize 6×6 36 36 36 36 36 36 36
    混沌控制系数μ 4 4 4
    混沌序列m 20 20 20
    惯性权重w 0.729 8
    信息素挥发系数 0.8
    转移概率常数 0.2
    下载: 导出CSV

    表  3   最佳参数

    Table  3   Optimal parameters

    算法 f a b
    本文算法 0.007 6 4.130 3 2.320 7
    文[11]算法 0.008 6 4.289 6 2.299 5
    文[7]算法 0.009 3 4.399 9 2.279 2
    混沌鸟群算法 0.009 4 4.384 8 2.320 5
    自适应鸟群算法 0.010 4 4.586 4 2.305 6
    BSA算法 0.010 3 4.562 6 2.279 6
    PSO算法 0.011 1 4.629 0 2.266 2
    ACO算法 0.013 4 4.610 8 2.279 1
    下载: 导出CSV

    表  4   统计结果

    Table  4   Statistical results

    算法 平均值 最小值 最大值 标准差
    本文算法 0.007 9 0.007 6 0.010 7 7.212 4×10-4
    文[11]算法 0.009 4 0.007 6 0.012 1 0.001 5
    文[7]算法 0.008 7 0.007 6 0.012 5 0.001 5
    混沌鸟群算法 0.012 3 0.007 8 0.035 1 0.005 4
    自适应鸟群算法 0.008 7 0.007 6 0.011 8 0.001 3
    BSA算法 0.009 3 0.007 6 0.012 7 0.001 3
    PSO算法 0.008 6 0.007 7 0.014 1 0.001 4
    ACO算法 0.011 2 0.009 6 0.014 5 0.001 4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-17
  • 录用日期:  2021-01-14
  • 发布日期:  2021-08-19
  • 刊出日期:  2021-08-19

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