Improved Genetic-Ant Colony Fusion Algorithm

Abstract

An improved genetic-ant colony fusion algorithm is proposed to improve the search speed of path planning and to obtain the optimal solution. Combining the advantages of the two algorithms, adaptive fetching is used for selection probability, crossover probability and variation probability respectively for the genetic algorithm to globally search for the optimal path. To address the problem that the number of iterations in the genetic algorithm relies too much on subjective experience, we propose to derive the evolutionary degree from the fitness function as a strategy to control the conversion of the algorithm. Finally, the optimal solution searched by the genetic algorithm is used as the value of the initial pheromone of the ant colony algorithm, and the crossover operation of the genetic algorithm is added to the ant colony algorithm, which can optimize the search speed . The experiments show that the fusion strategy can improve the path planning search speed and the accuracy of the candidate solutions.