Open AccessArtificial Intelligence Path Planning for Marine Environment
Author(s) -
Shuo Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1852/2/022043
Subject(s) - ant colony optimization algorithms , motion planning , path (computing) , computer science , mathematical optimization , swarm intelligence , convergence (economics) , shortest path problem , population , artificial intelligence , operations research , engineering , machine learning , mathematics , particle swarm optimization , robot , theoretical computer science , graph , demography , sociology , economics , programming language , economic growth
Artificial intelligence path planning and its optimization method is an important topic of the times, but the research of path planning for marine environment is less. Under the background of the popularization of offshore operation platform and the urgent development of marine resources, it is very important to analyze the artificial intelligence marine path planning and study the multi strategy path planning optimization algorithm. In view of this, this study proposes a multi strategy optimization ant colony algorithm (ACA-Mso), and realizes the artificial intelligence path planning of marine environment by updating the population pheromone strategy, implementing the preview strategy of local forced exchange optimization, and the symmetric path optimization strategy. Its main advantage is that it not only overcomes the shortcomings of the basic ant colony algorithm, such as long convergence time, too many iterations of the optimal solution, and the limitations of fuzzy symmetric path selection rules, but also ensures the enhancement of pheromone among nodes, the optimization of local search path potential, and the improvement of search ability of path planning algorithm, so as to realize the comprehensive path planning of marine environment Optimization.