Wireless Sensor Network Path Optimization Based on Hybrid Algorithm

One merit of genetic algorithm is fast overall searching, but this algorithm usually results in low efficiency because of large quantities of redundant codes. The advantages of ant colony algorithm are strong suitability and good robustness while its disadvantages are tendency to stagnation, slow speed of convergence. Put forward based on improved ant colony algorithm for wireless sensor network path optimization approach will first need to pass the data in the shortest path for transmission, assuming that transmission path jam, it will clog information sent to the initial position, so the follow-up need to pass data can choose other reasonable path so as to avoid the defects of the traditional method. Genetic ant colony is proposed to avoid the faults of both algorithms above. The proposed algorithm determines distribution of pheromones on path through fast searching and changing the operation of selection operator, crossover operator and mutation operator of genetic ant colony, and then solves the problems efficiently through parallelism, positive feedback and iteration of ant colony algorithm. Therefore, the faults of both algorithms are conquered and the aim of combinational optimization is achieved. At last, the validity and feasibility is demonstrated by means of simulation experiment of traveling salesman problem.