Path planning chart for Environment 1 (10 × 10, destination containing obstacles).

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0329504#pone.0329504.g012" target="_blank">Fig 12</a> demonstrates the path planning performance in a 10 × 10 environment from start point (1,1) to target (7,9). The traditional APF method...

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Bibliographic Details
Main Author:	Tengfei Ma (597633) (author)
Other Authors:	Guangda Lu (21661844) (author), Zhuanping Qin (21661838) (author), Tinghang Guo (21989505) (author), Zheng Li (26302) (author), Changli Zhao (7114091) (author)
Published:	2025
Subjects:	Biophysics Medicine Molecular Biology Evolutionary Biology Plasma Physics Plant Biology Computational Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified plo ), red planned path length good point set cec2017 test functions bloodsucking leech algorithm artificial potential field algorithm &# 8217 291 %) compared zebra optimization algorithm enhanced optimization capability eliminates information silos div >< p applications </ p zoa &# 8217 results confirm op position update process escaping local optima based learning mechanism local optima based learning optimization algorithms state information zoa )). vigilante agent verified using slow convergence significantly improving randomly selecting pooling strategy pooling strategies paper proposes overall robustness optimal individual inadequate exploration heuristic algorithms global optimum facilitating escape experimental evaluation dynamic elite convergence speed
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Path planning chart for Environment 1 (10 × 10, destination containing obstacles).

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