Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning

Abstract

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm's possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

Keywords: Dimension Learning-Based Hunting search strategy; Dung Beetle Optimizer algorithm; Harris Hawk Optimization algorithm; double adaptive weight strategy; path planning.

Figure 1

MIHHO algorithm flow chart.

Figure 2

Classical functions convergence curves. (a) F1 convergence curve; (b) F2 convergence curve; (c) F3 convergence curve; (d) F4 convergence curve; (e) F5 convergence curve; (f) F6 convergence curve; (g) F7 convergence curve; (h) F8 convergence curve; (i) F9 convergence curve; (j) F10 convergence curve; (k) F11 convergence curve; (l) F12 convergence curve.

Figure 3

Boxplots of MIHHO algorithm with other comparative algorithms. (a) F1 test function; (b) F2 test function; (c) F3 test function; (d) F4 test function; (e) F5 test function; (f) F6 test function; (g) F7 test function; (h) F8 test function; (i) F9 test function; (j) F10 test function; (k) F11 test function; (l) F12 test function.

Figure 4

CEC2022 functions’ convergence curves. (a) F13 convergence curve; (b) F14 convergence curve; (c) F15 convergence curve; (d) F16 convergence curve; (e) F17 convergence curve; (f) F18 convergence curve; (g) F19 convergence curve; (h) F20 convergence curve; (i) F21 convergence curve; (j) F22 convergence curve; (k) F23 convergence curve; (l) F24 convergence curve.

Figure 5

Boxplot of MIHHO algorithm compared with other algorithms. (a) F13 test function; (b) F14 test function; (c) F15 test function; (d) F16 test function; (e) F17 test function; (f) F18 test function; (g) F19 test function; (h) F20 test function; (i) F21 test function; (j) F22 test function; (k) F23 test function; (l) F24 test function.

Figure 5

Boxplot of MIHHO algorithm compared with other algorithms. (a) F13 test function; (b) F14 test function; (c) F15 test function; (d) F16 test function; (e) F17 test function; (f) F18 test function; (g) F19 test function; (h) F20 test function; (i) F21 test function; (j) F22 test function; (k) F23 test function; (l) F24 test function.

Figure 6

Function convergence curves. (a) F1 convergence curve; (b) F2 convergence curve; (c) F3 convergence curve; (d) F4 convergence curve; (e) F5 convergence curve; (f) F6 convergence curve; (g) F7 convergence curve; (h) F8 convergence curve; (i) F9 convergence curve; (j) F10 convergence curve; (k) F11 convergence curve; (l) F12 convergence curve.

Figure 6

Function convergence curves. (a) F1 convergence curve; (b) F2 convergence curve; (c) F3 convergence curve; (d) F4 convergence curve; (e) F5 convergence curve; (f) F6 convergence curve; (g) F7 convergence curve; (h) F8 convergence curve; (i) F9 convergence curve; (j) F10 convergence curve; (k) F11 convergence curve; (l) F12 convergence curve.

Figure 7

Convergence curves of ablation experiments. (a) F1 convergence curve; (b) F2 convergence curve; (c) F3 convergence curve; (d) F6 convergence curve; (e) F8 convergence curve; (f) F13 convergence curve; (g) F14 convergence curve; (h) F18 convergence curve; (i) F21 convergence curve.

Figure 7

Convergence curves of ablation experiments. (a) F1 convergence curve; (b) F2 convergence curve; (c) F3 convergence curve; (d) F6 convergence curve; (e) F8 convergence curve; (f) F13 convergence curve; (g) F14 convergence curve; (h) F18 convergence curve; (i) F21 convergence curve.

Figure 8

The simulation results of path planning in simple environments. (a) The simulation results of path planning in a 20 × 20 grid map in a simple environment. (b) The simulation results of path planning in a 40 × 40 grid map in a simple environment.

Figure 9

Convergence curves for path planning in simple environments. (a) The convergence curves of each algorithm in a 20 × 20 grid map in a simple environment. (b) The convergence curves of each algorithm in a 40 × 40 grid map in a simple environment.

Figure 10

The simulation results of path planning in complex environments. (a) The simulation results of path planning in a 20 × 20 grid map in a complex environment. (b) The simulation results of path planning in a 40 × 40 grid map in a complex environment.

Figure 11

Convergence curves for path planning in complex environments. (a) The convergence curves of each algorithm in a 20 × 20 grid map in a complex environment. (b) The convergence curves of each algorithm in a 40 × 40 grid map in a complex environment.