Dynamic target search algorithm for AUV based on improved genetic algorithm

Abstract

For the underwater dynamic target searching problem, an AUV dynamic target search algorithm based on improved genetic algorithm is proposed. The Monte Carlo method is used to generate a large number of target motion trajectories as the basis for calculating the fitness; a novel way to calculate the cumulative detection probability is proposed in combination with the hydroacoustic model ; the population selection adopts a combination of catastrophe idea and elitism method to ensure the non-inferiority and diversity of the population and accelerate the jump out of local extremes; chaotic sequence is used to select the crossover points and variation points to increase the population randomness; adopting dynamic adaptive crossover probability and variation probability reduce empirical dependence and ensure population diversity in the later stage. The simulation experimental results show that the improved genetic algorithm can effectively avoid falling into local extremes and improve the search probability compared with the traditional algorithm and classical genetic algorithm.

Keywords

• dynamic target search|improved genetic algorithm|catastrophe idea|chaotic sequence|cumulative detection probability