This paper addresses the collaborative path planning of multiple Autonomous Underwater Vehicles (AUVs) in underwater environments, a critical issue for efficient and effective marine applications. The authors propose an Adaptive Multi-Population Particle Swarm Optimization (AMP-PSO) algorithm to enhance the global search capability, convergence speed, and adaptability in complex environments. AMP-PSO introduces a grouping strategy that separates particles into a leader population and various follower populations based on fitness, with particles in the leader population updated by both the leader and follower populations to maintain global optimization, while particles in the follower populations are updated by their own group to prioritize local exploration. An exchanging mechanism allows particles to move between groups, improving diversity. The algorithm also includes adaptive parameter configuration to enhance performance. Numerical experiments in various scenarios demonstrate that AMP-PSO outperforms classic PSO and other improved PSO algorithms in terms of path quality, computational efficiency, and adaptability to complex environments. The results show that AMP-PSO can generate feasible and optimal paths, enabling multiple AUVs to avoid collisions and meet various constraints effectively.This paper addresses the collaborative path planning of multiple Autonomous Underwater Vehicles (AUVs) in underwater environments, a critical issue for efficient and effective marine applications. The authors propose an Adaptive Multi-Population Particle Swarm Optimization (AMP-PSO) algorithm to enhance the global search capability, convergence speed, and adaptability in complex environments. AMP-PSO introduces a grouping strategy that separates particles into a leader population and various follower populations based on fitness, with particles in the leader population updated by both the leader and follower populations to maintain global optimization, while particles in the follower populations are updated by their own group to prioritize local exploration. An exchanging mechanism allows particles to move between groups, improving diversity. The algorithm also includes adaptive parameter configuration to enhance performance. Numerical experiments in various scenarios demonstrate that AMP-PSO outperforms classic PSO and other improved PSO algorithms in terms of path quality, computational efficiency, and adaptability to complex environments. The results show that AMP-PSO can generate feasible and optimal paths, enabling multiple AUVs to avoid collisions and meet various constraints effectively.