基于异步爬猴群算法的传感器优化布置方法研究

针对猴群算法中的重要步骤"爬过程"搜索盲目、效率较低的问题,提出了一种用于传感器优化布置的异步爬猴群算法。采用双重编码的方式,克服了原猴群算法只能解决连续变量优化问题的缺陷;利用猴群在搜索过程中的全局最优解和个体历史最优解的信息改进了爬过程的搜索模式,同时将异步变化学习因子引入到搜索模式中,通过调整猴子自身经验和社会群体经验在爬过程中所起的作用,来保持全局搜索和局部搜索的平衡,大幅提高了算法的搜索效率。文末以广州新电视塔为例,进行了参数敏感性分析以及传感器优化布置方案的选择。结果表明,异步爬猴群算法能较好的解决传感器优化布置问题,搜索效率较原猴群算法有了较大的提高。

Asynchronous-climb monkey algorithm for optimal sensor placement

A novel asynchronous-climb monkey algorithm for optimal sensor placement is presented to solve the problems of sightless search and low efficiency in the key climb process of the monkey algorithm.The dual-structure coding method is adopted to overcome the disadvantage that the original monkey algorithm can only perform the optimization on continuous variables.The search pattern is improved by the information of global optimal solution and previous best solution during the search process of monkey population.Meanwhile,the asynchronous variable learning factor is introduced into the search pattern to maintain the balance of global and local search by adjusting the effect of monkeys' own and social experiences during the climb process,which greatly improve the search efficiency of the algorithm.Finally,the parametric sensitivity analysis and selection of optimal sensor placement are performed on Guangzhou new TV tower.The results show that the asynchronous-climb monkey algorithm is efficient and effective for sensor placement problem compared to the monkey algorithm.