基于模拟退火的混合萤火虫Memetic算法  

Hybrid firefly Memetic algorithm based on simulated annealing

作　　者：刘翱[1,2] 邓旭东[1] 李维刚[3,4] 

机构地区：[1]武汉科技大学管理学院,武汉430081 [2]智能信息处理与实时工业系统湖北省重点实验室,武汉430065 [3]武汉科技大学信息科学与工程学院,武汉430081 [4]冶金工业过程系统科学湖北省重点实验室,武汉430081

出　　处：《计算机应用》2016年第11期3055-3061,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(11271356);教育部人文社会科学研究青年基金资助项目(16YJCZH056);智能信息处理与实时工业系统湖北省重点实验室开放基金资助项目(2016znss18B);冶金工业过程系统科学湖北省重点实验室开放基金资助项目(Z201501);武汉科技大学青年科技骨干培育计划项目(2016xz017)~~

摘　　要：针对标准萤火虫算法(FA),首先,从数学理论上分析并揭示了其存在的种群过早收敛、容易陷入局部最优等不足,然后提出一种基于模拟退火的混合萤火虫Memetic算法。该算法利用标准萤火虫算法对上一代种群进行全局搜索以保持种群的多样性和算法的全局探索能力;使用模拟退火算子对当前种群中的部分个体进行局部搜索,以一定概率接受适应度较差的个体以避免算法陷入局部最优,该算法同步进行萤火虫吸引过程和模拟退火过程以降低算法复杂度。最后,对该算法在10个标准测试函数上进行对比仿真实验。实验结果表明,该算法在6个测试函数中均能找到最优解,最优值、平均值、方差等指标比对比算法高出一定数量级,在4个复合函数中效果均优于萤火虫算法。A mathematical analysis was carried out theoretically to reveal the fact that the Firefly Algorithm （FA） gets the risk of premature convergence and being trapped in local optimum. A hybrid Memetic algorithm based on simulated annealing was proposed. In the hybrid algorithm, the FA was employed to keep the diversity of firefly population and global exploration ability of the proposed algorithm. And then, the simulated annealing operator was incorporated to get rid of local optimum, which was utilized to carry out local search with partial firefly individuals by accepting bad solutions with some probability, and the proposed algorithm conducted simultaneously the attracting process and the annealing process to reduce the complexity. Finally, the performance of the proposed algorithm and other comparison algorithms were tested on ten standard functions, respectively. The experimental results show that the proposed algorithm can find the optimal solutions in six functions, outperform firefly algorithm, particle swarm optimization, etc, in terms of optimal value, mean value and standard deviation, and find better solutions than firefly algorithm in four functions.

关 键 词：模拟退火 萤火虫算法 局部搜索 MEMETIC算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]