摘要
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.
作者
徐梦颖
卢毅
周杰
Xu Mengying;Lu Yi;Zhou Jie(College of Information Science and Technology,Shihezi University,Shihezi 832000,China)
出处
《电子技术应用》
2020年第7期94-98,共5页
Application of Electronic Technique
基金
国家自然基金项目(61662063)
兵团中青年科技创新领军人才计划项目(2018CB006)
兵团重大科技项目(2017AA005-04)
石河子大学研究生教育教学改革项目(2019Y-JGFF03)。
关键词
无线传感器网络
目标覆盖
混合蛙跳算法
混沌
免疫
遗传算法
粒子群算法
收敛速度
wireless sensor networks(WSNs)
target coverage
shuffled frog leaping algorithm
chaos
immune
genetic algorithm
particle swarm optimization
convergence speed
