Abstract
The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.
Highlights
An unmanned surface vehicle (USV) is an intelligent surface mission platform
The state changes in the operation based on the colony sequence {A(n), n ≥ 0} of the immune algorithm (IA)-improved ant colony optimization (IACO) algorithm happen in the limited space
When the ant colony algorithm is used for path planning, it is prone to the problem of slow convergence speed and falling into local optimum
Summary
An unmanned surface vehicle (USV) is an intelligent surface mission platform. It features high speed, intelligence, good concealment, flexibility, low cost, great resistance to extreme conditions, and no risk of casualty. It can carry different loads for fulfilling all kinds of assignments and shows great prospects in the military and civilian fields.. Li and Teng introduced the directional function to improve the heuristic function, combined the ways for updating local and global pheromones, and took into account the influence of safety distance Their effort led to the improved convergence speed of the algorithm and the shorter distance of the path. Zhang et al. adopted the adaptive adjustment and pseudo-stochastic state transition strategy to change the parameters and prevent the premature problem of the algorithm They improved the convergence speed of the algorithm based on the adaptive pheromone updating. Wang et al. brought forward a volatilization factor adaptive strategy and improved the distance evaluation function of heuristic information for faster searching and less iteration These references present the improvement approaches to enhance the convergence speed and optimization capability of the ACO algorithm to some extent but lack the analysis on the systematic improvement and convergence of the algorithm.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
