求解高维函数的改进萤火虫群优化算法  

Improved glowworm swarm optimization algorithm for high-dimensional functions

作　　者：彭硕[1,2] 欧阳艾嘉[3] 乐光学[4] 贺明华[5] 周旭[3,4] 

机构地区：[1]井冈山大学电子与信息工程学院,江西吉安343009 [2]同济大学软件学院,上海200092 [3]湖南大学信息科学与工程学院,长沙410082 [4]嘉兴学院数理与信息工程学院,浙江嘉兴314001 [5]井冈山大学高等教育研究所,江西吉安343009

出　　处：《计算机应用》2013年第8期2253-2256,2260,共5页journal of Computer Applications

基　　金：国家科技支撑计划项目(2012BAC11B01);国家自然科学基金资助项目(61202109);浙江省自然科学基金资助项目(LY12F02019);浙江省科技计划项目(2011C23130);井冈山大学科研基金资助项目(JR1216;JZ11003);吉安市科技项目(2011327)

摘　　要：针对萤火虫群优化(GSO)算法求解高维函数时存在求解精度不高、收敛速度慢等缺点,提出了一种带变异算子和集群觅食行为算子的改进萤火虫群优化算法。该算法使用变异算子来指导离群萤火虫的进化方向,从而提高了离群个体的利用率,改善了算法的整体效率。集群觅食行为算子的加入能使算法对捕捉到的全局最优域进行更进一步的求精,极大地提高了算法的计算精度和收敛速度;同时,该算子有效地防止了算法陷入局部最优值的危险,扩大了算法在后期的全局搜索范围。通过8个典型的基准函数测试,结果表明:改进后萤火虫群优化算法具有更强的全局优化能力和更高的成功率。Concerning the low accuracy and convergence of Glowworm Swarm Optimization（GSO） algorithm when resolving high-dimensional functions,an Improved GSO（IGSO） algorithm with mutation operator and foraging behavior was proposed.Using mutation operator to guide the evolution of glow worms which cannot find their peers in the visible range,the proposed algorithm could enhance the utilization of outliers and improve the overall efficiency.The operator with foraging behavior substantially increased the accuracy and convergence speed by searching accurately in the global optimal field captured by the algorithm.In the meantime,the operator could effectively avoid local optimum and enlarge the global search range of the algorithm in the late stage.The experimental results indicate that IGSO has better ability of global optimization and higher success ratio than GSO according to the tests of eight Benchmarks.

关 键 词：全局优化 萤火虫群优化算法 变异算子 觅食行为 高维函数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]