Research on Robot Path Planning Based on Improved Harris Hawks Algorithm

doi:10.16182/j.issn1004731x.joss.23-1342

Abstract

Abstract:

In order to improve the convergence accuracy of the HHO algorithm, this paper proposes a GSHHO(gold sine harris hawks optimization) algorithm based on multi-strategies. An infinite iterative chaotic map is used to initialize the population, and an elite reverse learning strategy is used to improve population quality; A convergence factor adjustment strategy is used to recalculate prey energy, balancing the global exploration and local development capabilities of the algorithm; In the development phase of Harris Eagle, the golden sine strategy was introduced to replace the original position update method and improve the local development ability of the algorithm; Experiments were conducted to evaluate the optimization performance of GSHHO. Experimental results show that the path length of GSHHO is reduced by 4.4% and 3.17% respectively and the stability is increased by 52.98% and 63.12% respectively compared with the original HHO algorithm.

Key words: HHO algorithm, iterative chaos, elite reverse learning, golden sine algorithm, grid method, path planning

CLC Number:

TP391.9

Bai Yuxin, Chen Zhenya, Shi Ruitao, Su Weitao, Ma Zhuoqiang, Yang Shangjin. Research on Robot Path Planning Based on Improved Harris Hawks Algorithm[J]. Journal of System Simulation, 2025, 37(3): 742-752.

Figures/Tables 9

Fig. 1

Fig. 2

Table 1

Nine test functions

函数类别	表达式	取值范围
单峰测试函数	$F 1 (x) = ∑ i = 1 30 x i 2$	(-100，100)
	$F 2 (x) = ∑ i = 1 30 ∑ j = 1 i x j 2$	(-100，100)
	$F 3 (x) = ∑ i = 1 29 100 (x i + 1 - x i 2) + (x i - 1) 2$	(-30，30)
多峰测试函数	$F 4 (x) = - ∑ i = 1 30 x i s i n x i$	(-100，100)
	$F 5 (x) = ∑ i = 1 30 x i 2 - 10 c o s (2 π x i) + 10$	(-10，10)
	$F 6 (x) = 1 4 000 ∑ i = 1 30 x i 2 - ∏ i = 1 30 c o s x i i + 1$	(-100，100)
固定维多峰测试函数	$F 7 (x) = 1500 + ∑ j = 1 25 1 j + ∑ i = 1 2 (x i - a i j) 6 - 1$	(-65.536，65.536)
	$F 8 (x) = - ∑ i = 1 4 c i e x p - ∑ j = 1 6 a i j (x j - p i j) 2$	(0，1)
	$F 9 (x) = - ∑ i = 1 10 (x - a i) (x - a i) T + c i - 1$	(0，10)

Table 1

Table 2

Fig. 3

Table 3

Fig. 4

Fig. 5

Table 4

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函数	指标	GWO	WOA	HHO	GA	PSO	GSHHO
F1	最优值	1.400 1×10^-125	2.765 4×10^-218	2.765 4×10^-218	0.025 4	2.935 9×10^-11	2.765 4×10^-218
	平均值	3.113 5×10^-117	1.980 6×10^-156	3.017 2×10^-189	1.470 2	3.014 2×10^-9	6.717 5×10^-191
	标准差	9.360 5×10^-117	6.240 4×10^-156	0	2.087 3	5.743 6×10^-9	0
F2	最优值	4.557 1×10^-65	1.546×10^-193	1.546×10^-193	8.330 3×10²	1.338 3×10^-4	1.546 0×10^-193
	平均值	9.318 8×10^-52	18.815 4	1.144 6×10^-163	3.982 3×10³	0.001 8	1.853 0×10^-165
	标准差	5.033 1×10^-51	52.093 2	9.987 6×10^-161	2.398 5×10³	0.001 9	0
F4	最优值	-3.716 0×10³	-4.189 8×10³	-4.189 8×10³	-1.845 4×10³	-3.064 7×10³	-4.189 8×10³
	平均值	-2.804 3×10³	-3.434 4×10³	-4.164 8×10³	-1.228 6×10³	-2.493 1×10³	-4.189 8×10³
	标准差	3.403 2×10²	5.785 2×10²	1.369 9×10²	2.7755×10²	3.672 1×10²	0.033 0
F6	最优值	0.998 0	0.998 0	0.998 0	0.998 0	0.998 0	0.998 0
	平均值	5.107 7	3.314 9	1.196 8	1.023 4	3.103 3	0.998 0
	标准差	4.441 2	3.858 7	0.404 4	0.129 7	2.322 3	2.375 4×10^-10
F8	最优值	-3.322 0	-3.315 6	-3.315 6	-2.607 5	-3.322 0	-3.410 2
	平均值	-3.256 5	-3.256 9	-3.122 3	-1.608 8	-3.278 4	-3.383 7
	标准差	0.069 8	0.089 1	0.097 3	0.548 3	0.058 3	0.072 3
F9	最优值	-10.536 4	-10.536 3	-10.536 3	-2.123 4	-10.536 4	-10.536 6
	平均值	-10.535 9	-7.911 9	-5.302 8	-1.217 0	-9.862 5	-10.588 1
	标准差	3.030 3	3.098 7	0.960 9	0.422 7	2.086 7	0.077 6

地图	实验次数	GA	PSO	GWO	WOA	HHO	GSHHO
简单	1	28.577 7	29.827 2	28.577 7	31.964 0	28.577 7	28.419 3
	2	28.531 2	31.052 5	28.577 7	30.809 8	30.484 9	28.139 5
	3	31.052 5	29.411 7	29.892 6	31.424 7	28.577 7	28.577 7
	4	28.725 2	29.411 7	28.419 3	29.890 2	31.052 5	28.419 3
	5	30.533 9	28.577 7	28.577 7	32.339 9	30.475 2	28.577 7
	6	29.233 5	29.411 7	28.577 7	31.655 4	29.264 6	28.139 5
	7	30.188 5	30.188 5	30.188 5	33.095 2	30.767 4	28.631 5
	8	31.052 5	30.188 5	29.954 7	33.090 8	30.297 6	29.901 7
	9	29.954 7	31.073 3	28.577 7	30.282 0	28.577 7	28.577 7
	10	30.892 7	28.577 7	28.725 2	32.483 8	31.052 5	28.577 7
复杂	1	60.566 8	65.052 5	64.069 5	76.828 4	69.022 7	58.925
	2	63.927 5	61.259 2	63.434 7	76.828 4	60.784 9	59.562 1
	3	61.165 8	70.924 4	65.277 0	75.656 9	62.269 9	61.326 7
	4	66.620 5	62.837 5	61.699 2	76.828 4	67.363 0	61.240 7
	5	59.891 9	66.227 2	59.891 0	76.828 4	68.086 0	59.708 6
	6	60.532 0	71.424 3	66.544 3	75.656 9	63.954 6	59.804 9
	7	73.447 9	71.317 6	78.519 7	76.828 4	59.985 6	58.241 0
	8	59.522 4	62.891 8	59.669 0	76.828 4	61.778 5	59.141 0
	9	60.572 1	67.537 7	63.545 4	76.242 6	65.359 1	58.082 5
	10	61.820 5	62.561 2	62.506 0	76.828 4	64.570 5	60.870 2

地图环境	算法	最优值	平均值	标准差
简单	GA	28.531 2	29.874 2	0.978 3
	PSO	28.577 7	29.772 1	0.832 2
	GWO	28.419 3	29.006 9	0.665 2
	WOA	29.890 2	31.703 6	1.055 0
	HHO	28.577 7	29.912 8	0.994 4
	GSHHO	28.139 5	28.596 2	0.467 6
复杂	GA	62.806 7	59.522 4	4.088 6
	PSO	66.203 3	61.259 2	3.725 8
	GWO	64.515 6	59.669	5.098 7
	WOA	76.535 5	75.656 9	0.472 2
	HHO	64.317 5	59.985 6	2.979 6
	GSHHO	59.690 3	58.082 5	1.098 9

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