Altruistic nurturing algorithm for multi-objective autonomous underwater vehicles path planning problems

Solving the path planning problem of Autonomous Underwater Vehicles (AUVs) is crucial for reducing energy waste and improving operational efficiency. However, two main challenges hinder further development: Firstly, existing algorithms often treat this as a single-objective optimization problem, whereas in reality, it should be multi-objective, considering factors such as distance, safety, and smoothness simultaneously. Secondly, the limited availability of optimization results arises due to they are single-path, which fail to meet real-world conditions. To address these challenges, first of all, an improved AUV path planning model is proposed, in which the collisions of path and obstacles are classified more specifically. Subsequently, a novel Altruistic Nurturing Algorithm (ANA) inspired by natural altruism is introduced. In the algorithm, nurturing cost considering Pareto rank and crowd distance is introduced as guidance of evolution to avoid futile calculation, abandonment threshold is self-adaptive with descendant situation to help individuals escape from local optima and double selection strategy combining crowd and k-nearest neighbors selection helps to get a better-distributed Pareto front. Experimental results comparing ANA with existing algorithms in AUV path planning demonstrate its superiority. Finally, a user-friendly interface, the Multi-Objective AUV Path Planner, is designed to provide users with a group of paths for informed decision-making.