A path planning algorithm based on genetic and ant colony dynamic integration

Genetic algorithm has strong global search ability and robustness, but can not use the feedback of system and easy to do a lot of unnecessary redundancy iteration in the latter, resulting in the decline of convergence rate. Ant colony algorithm has the good characteristic of feedback, but due to lacking of initial pheromone, solving speed is slow. A method of adaptive dynamic integration is presented in the paper based on two algorithms, it uses genetic algorithm to generate initial pheromone distribution, and uses ant colony algorithm for dynamic integration of genetic operators in the latter. It not only improves the convergence rate of solutions, but also solves the problem of precocious and poor global search ability caused by the excessive convergence of ant colony algorithm in the late.

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