Ant Colony Optimization Methods And Its Applications In Navigation Planning For Submarine

Submarine navigation planning is one of the key technologies in Submarine automation and intelligent navigation. The aim of its research is how to make submarine accomplish the mission from upper command fasterly and better; according to the optimization objective of safe, hidden, fast navigation, planning automatically optimum route from recent point to intended target point. In the process of algorithm execution, the navigation planning mainly composese by two parts, environment modeling and path optimization search. Thus, the problem of navigation planning can be regarded as an optimization search.Ant colony optimization algorithm is a swarm intelligence optimization algorithm proposed in the early 1990s. The superiority of distributed solution model of problem in solving combinatorial optimization problem is very great, and this causese scientists' large attention in concerned fields. The practice has shown that, ant colony optimization algorithm can well solve problems of multi-constraint optimization under complicated nonlinear conditions. This paper is mainly about ant colony optimization algorithm and its application in submarine navigation planning, including:1. The basic principle of the ant colony optimization algorithm is summarized, and the convergence of value and solution for the standard ACS algorithm is analysed in detail. It can not prove the ablitiy of the solution convergence, but the value convergence can. By improving the standard ACS algorithm, choosing appropriate lower function of pheromone value, we can get both of them.2. The general representation of state transition strategy is given. We also propose three concepts of ant colony algorithm: selection function, selection probability and selection intensity, then design three selection functions. To each function, theory and simulation analysis on influence of performance of ant colony algorithm have been done. A parameter selection method of ant colony algorithm is proposed. Based on particle swarm optimization, it regards parameter selection as an optimization problem, then iterates particle swarm optimization algorithm for optimization. This method can realize the optimization of ant colony algorithm and is good for its application and popularization.3. In order to inproving the optimization ability and speed in ant colony algorithm in discrete domain, we design two algorithms: virtual parallel ant colonies optimization algorithm based on cooperative multiple ant colonies and ant colonies optimization algorithm based on space contraction. In the former one, many children ant colonies adopted different case algorithm model consist the problem solution in concurrent and independence. It can ensure the instructive and diversity of pheromone distribution by synthetizing experience information from each pheromone matrix of children ant colonies through information interaction, and it can improve the optimization ability and stability. In the latter one, it can reduce the solution compositions and the scale of structure block set by continually integrating the structure block with thick pheromones in execute process, and it can work faster.4. Based on deep analysis of relationship between ant colony distribution and food source in ant foraging process, an improved ant colony algorithm in continuous domain based on ant foraging behavior is proposed. We design the representation of algorithm solution, pheromone distribution model, state transition strategy, pheromone update rules and processing method of constraint conditions. Then, influence of algorithm performance to parameters is qualitatively analyzed. The results of one kind of benchmark test function with or without constraints show that, this algorithm is converged faster and has strong global optimization ability.5. According to the deep research of way and key technologies of submarine navigation planning in three dimensional spaces based on ant colony optimization, we design submarine navigation planning algorithms in three dimensional space, which is respectively based on space contraction ant colonies optimization algorithm and ant colony algorithm in continuous domain. These two algorithms for navigation planning not only can plan neatly optimization path with different characters, but also can deal with different constraints. It has srong searching ability and can work out the problem of submarine navigation planning in three dimensional spaces perfectly.