Mechanism Optimization Based on Multi-agent Chaos Bird Swarm Algorithm
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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.
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关键词:
- 多智能体混沌鸟群算法 /
- 多智能体系统 /
- 混沌搜索 /
- Logistic映射 /
- 机构优化
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.
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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 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c1,c2 1.5 参见式(26) 1.5 1.5 参见式(27) 1.5 1.496 18 a1,a2 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 表 3 最佳参数
Table 3 Optimal parameters
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 -
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