一种无线传感器网络中的目标覆盖优化算法

An optimization algorithm for target coverage in wireless sensor networks

为了优化无线传感器网络中成功监测到的目标个数,设计了目标覆盖模型,提出了改进的混沌免疫混合蛙跳算法(Improved Chaotic Immune Shuffled Frog Leaping Algorithm,ICISFLA)。该算法使用混沌序列对种群进行编码,从而增加种群的多样性;使用免疫算子将种群中适应度较高的个体保留至下一代;使用变异算子改进种群中适应度最低青蛙的学习机制,从而改善局部最优解和全局最优解。为了验证该算法的性能,将该算法与粒子群算法、遗传算法进行比较。仿真结果显示,与其他两种算法相比,ICISFLA的收敛速度更快,被成功监测到的目标数量显著增加。

In order to optimize the number of successfully monitored targets in WSNs,a target coverage model is designed and an improved chaotic immune shuffled frog leaping algorithm(ICISFLA)is proposed.The chaotic sequence is used to initialize the frogs to increases the diversity of the population.The immune operator is used to select individuals with higher fitness in the population to inherit to the next generation.The mutation operator is used to improve the learning mechanism of the frog with the lowest fitness in the population.Moreover,the local optimal solution and the global optimal solution can be improved.In order to verify the performance of the proposed algorithm,it is compared with the particle swarm optimization(PSO)and genetic algorithm(GA).The simulation results show that the proposed algorithm has a faster convergence speed than GA and PSO.The number of successfully monitored targets optimized by ICISFLA has increased dramatically.