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

Artificial glowworm swarm optimization algorithm based on biological predator-prey behavior

针对基本萤火虫优化(GSO)算法在求解函数全局最优值时,存在着易陷入局部最优、收敛速度慢和求解精度低等问题,提出了1种基于生物捕食-被捕食(Predator-Prey)行为的双种群GSO算法(GSOPP)。该算法通过引入种群间的追逐与逃跑以及变异等策略加快了收敛速度,且能获得精度更高的解。最后,通过对8个标准测试函数进行测试,结果表明,改进后的GSOPP算法比基本GSO算法有更优的性能。According to the basic glowworm swarm optimization （GSO） algorithm in solving the function of global optimal value existing some problems, such as easy to fall into local optimum, slow convergence and low precision, an artificial glowworm swarm optimization algorithm based biological predator-prey behavior （GSOPP） is proposed. The algorithm through populations chase and escape, and the mutation strategy to speed up the convergence rate, and can obtain a more accurate solution. Finally, the test results of 8 standard test functions show that, the improved GSOPP algorithm than the basic GSO algorithm has Better performance.中国博士后基金(2012M511711);广西教育厅项目(201204LX082);广西民族大学项目(2011MDYB030);广西混杂计算与集成电路设计分析重点实验室开放基金(2012HCI09

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