一种面向异质多移动机器人的改进猫群算法

doi:10.16182/j.issn1004731x.joss.23-0783

摘要/Abstract

摘要：

目前的多移动机器人群组很难做到成员的真正同质，现有的群智能算法也很难包容团队的异质性。关注多移动机器人的异质性协同，提出母子机器人概念。为实现群智能算法在异质多移动机器人中的应用，改进了基本的猫群算法。定义了猫群的子域和邻域，提出了搜索方向的优先级、机器人的扩展轨迹跟踪、搜索空间的吸引力和候选搜索区域等8个方面的猫群算法改进。实验结果表明：母子机器人概念可以实现异质多移动机器人的目标搜索，可以包容团队成员的异质性，验证了改进猫群算法的适用性和有效性。

关键词: 多移动机器人, 猫群算法, 异质, 子域, 搜索方向

Abstract:

At present, it is difficult to achieve the real homogeneity of the members of the multiple mobile robots. The existing swarm intelligence algorithm is also difficult to accommodate the heterogeneity of the team. Focusing on the heterogeneous cooperation of multiple mobile robots, the concept of mother and child robots is proposed. In order to realize the application of swarm intelligence algorithm in heterogeneous multiple mobile robots, the basic cat swarm algorithm is improved. The sub-domain and neighborhood of cat swarm are defined, and eight improvements of cat swarm algorithm are proposed, including priority of search direction, extended trajectory tracking of robot, attraction of search space and candidate search area. The experimental results show that the concept of mother-child robot can realize the target search of heterogeneous multiple mobile robots, and can contain the heterogeneity of team members and verify the applicability and effectiveness of the improved cat swarm algorithm.

Key words: multiple mobile robots, cat swarm optimization, heterogeneous, sub-domain, search direction

中图分类号:

TP18

康亮,杜奕,尹丽华 . 一种面向异质多移动机器人的改进猫群算法[J]. 系统仿真学报, 2024, 36(8): 1958-1968.

Kang Liang,Du Yi,Yin Lihua . An Improved Cat Swarm Optimization for Heterogeneous Multiple Mobile Robots[J]. Journal of System Simulation, 2024, 36(8): 1958-1968.

图/表 7

图1

图2

图3

图4

图5

表1

测试函数CF1-CF6

Name	Definition	Global Minimum X*
CF1	$f (X) = ∑ i = 1 D x i 2$	$X * = (0, ⋯, 0)$
CF2	$f (X) = - 20 e x p - 0.2 1 / D ∑ i = 1 D x i 2 - e x p (1 / D ∑ i = 1 D c o s (2 π x i) + 20 + e x p (1)$	$X * = (0, ⋯, 0)$
CF3	$f (X) = ∑ i = 1 D - 1 [100 (x i + 1 - x i 2) 2 + (x i - 1) 2]$	$X * = (1, ⋯, 1)$
CF4	$f (X) = 10 D + ∑ i = 1 D [x i 2 - 10 × c o s (2 π x i)]$	$X * = (0, ⋯, 0)$
CF5	$f (X) = s i n 2 (π x i) + ∑ i = 1 D - 1 [(w i - 1) 2 (1 + 10 × s i n 2 (π w i + 1))] + (w d + 1) 2 [1 + s i n 2 (2 π w d)] w i = 1 + (x i - 1) / 4, i = 1, ⋯, d$	$X * = (1, ⋯, 1)$
CF6	$f (X) = ∑ i = 1 D (x i - 1) 2 - ∑ i = 2 D x i x i = 1$	$X i * = i (D + 1 - i), i = 1, ⋯, D$

表1

表2

参考文献 27

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		CF1	CF2	CF3	CF4	CF5	CF6
最小值	CSO	1.554 6×10^-11	1.875 8	1.594 9×10^-5	1.831 7×10²	1.812 3×10²	4.286 4×10³
	SCSO	1.498 8×10^-28	1.373 3	6.113 5×10^-5	1.622 8×10²	1.850 3×10²	1.357 0×10⁴
	MCSO	1.182 7×10^-150	7.539 5×10^-15	1.600 5×10	1.780 9×10	9.323 3×10^-33	7.476 5×10²
	本文CSO	0	4.340 9×10^-16	1.817 7×10^-4	8.138 7×10	9.403 3×10^-33	3.137 3×10^-1
中间值	CSO	1.835 7×10^-99	4.137 9	2.278 7×10¹	4.118 1×10²	1.438 1×10³	5.053 9×10³
	SCSO	4.518 4×10^-27	3.437 5	1.961 1×10¹	4.337 2×10²	5.941 6×10³	1.467 0×10⁴
	MCSO	1.348 5×10^-14	1.475 5×10^-14	2.919 5×10¹	3.870 3×10¹	9.323 3×10^-33	7.057 9×10³
	本文CSO	0	1.501 5	2.081 6	1.323 2×10²	1.110 7×10	2.102 9×10³
均值	CSO	1.402 4×10^-91	5.336 4	5.639 8×10¹	4.971 0×10²	2.130 0×10³	5.264 9×10³
	SCSO	9.157 3×10^-21	4.524 1	4.037 0×10³	5.439 5×10²	6.304 3×10³	1.437 0×10⁴
	MCSO	2.588 3×10^-143	3.477 5	1.417 9×10²	4.121 5×10¹	2.101 4×10^-32	8.507 8×10³
	本文CSO	9.445 3×10^-105	1.687 5	5.631 4	1.417 1×10²	2.527 7×10¹	2.324 5×10³
标准差	CSO	1.028 5×10^-90	4.135 4	1.221 8×10²	2.710 3×10²	2.154 9×10³	8.475 2×10²
	SCSO	6.544 7×10^-20	4.128 0	2.858 5×10⁴	3.513 3×10²	4.316 4×10³	3.472 3×10^-2
	MCSO	1.548 7×10^-14	6.455 1	4.873 1×10²	1.778 8×10¹	2.431 0×10^-32	7.043 5×10³
	本文CSO	2.888 1×10^-10	7.690 8×10^-1	5.688 3	3.010 4×10¹	3.666 6×10¹	3.021 5×10³

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