Work

An Improved Crested Porcupine Algorithm for Path Planning of Underwater Robot Fish

Year: 2025

Type: article

Abstract: This paper proposes an Improved Crested Porcupine Optimizer (ICPO) to enhance the efficiency and accuracy of path planning for underwater bionic fish—enabling more effective obstacle avoidance, foraging, and predator evasion. Specifically, ICPO integrates an elite reverse learning mechanism to enhance global search capability and reduce the risk of premature convergence. An improved initialization strategy accelerates the early exploration phase, while a dynamic radius contraction mechanism increases adaptability and solution accuracy during the convergence stage. Experimental results demonstrate that ICPO exhibits superior performance and faster convergence. ICPO consistently identifies optimal paths with fewer iterations, reduced path lengths, and improved computational efficiency, underscoring its suitability for underwater navigation. Its robust performance across varied environmental conditions further affirms its effectiveness and reliability as a path-planning algorithm for bionic fish.

Source: International Journal of Computational Intelligence Systems

DOI: 10.1007/s44196-025-01075-0

Authors: Biao Wang, Zhi Li, Ruilong Luo, Fang Wang, Xuan Liu

Institution: Shanghai Ocean University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,490

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China