一种自供电多传感器无线环境质量监测系统的设计

为了实时精确监测既定区域的环境质量状况,解决传统无线传感网因电池不耐用又不能及时更新而导致网络中断的问题,研究设计了一种自供电多传感器的无线环境质量监测系统。所研究设计的无线传感网节点利用太阳能光伏电池板发电完成自供电,并对最大功率点跟踪控制算法进行了优化改进,解决了因电池不能及时充电而导致的传感器节点工作异常的问题,实现了环境质量状况无人值守实时监测。利用数据融合算法,将多节点传感器采集到的数据进行数据融合,通过实验测试表明,所设计系统的测试数据和当地当天中国环境监测总站发布的环境质量状况数据基本吻合。复杂系统优化与大数据处理广西高校重点实验室科研课题资助(No.2017CSOBDP0103);;广西高校科学与技术研究项目(201012MS185);;贵州省科学与技术基金(LKS[2012]34

Firefly Algorithm for Solving 0-1 Knapsack Problem

将贪心策略和变异策略与萤火虫算法相结合,提出一种求解0-1背包问题的贪心萤火虫算法。通过增加贪心策略和变异策略,在一定程度上能使萤火虫跳出局部极值,提高算法的性能。通过对多个实例的仿真,将该算法与其它算法如贪心遗传算法、贪心微粒群算法进行对比,对比结果表明,该算法在求解0-1背包问题上具有更强约束处理能力和快速收敛效果。 Taking advantage of the standard firefly algorithm （FA） and combining with the characteristics of the 0-1 knapsack problem, this paper designs a firefly algorithm based on 0-1 knapsack problem. After experimental simulation, we verified the firefly algorithm＇ s feasibility and effectiveness to solve 0-1 knapsack problem. Finally, after many simulation experiments, this paper analyzes the influence of various parameters on the algorithm performance, reflected the importance of selection of key pa- rameters to the algorithm optimization.中国博士后基金项目(2012M511711);广西混杂计算与集成电路设计分析重点实验室开放基金项目(2012HCI08);广西教育厅基金项目(201204LX082);广西民族大学基金项目(2011MDYB030

Improved firefly algorithm based on simplex method and its application in solving non-linear equation groups

萤火虫算法(FA)是一种基于群体搜索的启发式随机优化算法,其模拟自然界中萤火虫利用发光的生物学特性而表现出来的社会性行为。针对萤火虫算法存在着收敛速度慢、易陷入局部最优、求解精度低等不足,利用单纯形法局部搜索速度快和萤火虫算法全局寻优的特点,提出一种基于单纯形法的改进型萤火虫算法(SMFA)。通过对标准测试函数以及非线性方程组的实验仿真,并与其他算法进行的对比分析表明,改进后的算法在函数优化方面有较强的优势,在一定程度上有效地避免了陷入局部最优,提高了搜索的精度。The firefly algorithm （ FA） is a heuristic random optimization algorithm based on groupization. It simulates the social behavior of firefly in the natural environment represented in its biological characteristics of shining. FA has disadvantages in global searching, such as slow convergence speed, high possibility of being trapped in local optimum and low solving precision. An improved FA based on the simplex method is proposed. The proposed method combines the characteristics of speedy local search of simplex method with the global optimization of firefly algorithm. The simplex method modifies the firefly, which is located at poor positions through its reflection, expansion and compression operation. However, it improves the diversity of individuals and avoids falling into local optimum and improves the precision of the algorithm. The results showed that through simulations of standard benchmark functions and nonlinear functions and contrasted with other algorithms, the improved algorithm has a strong advantage in function optimization. It also avoids trapping in local optimum and improves the calculation accuracy to a certain extent.国家自然科学基金资助项目(21466008);广西民族大学科研资助项目(2014MDYB030