基于t分布的自适应花授粉算法
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摘要
花授粉算法是一种新型启发式优化算法,算法存在易陷入局部最优、后期收敛速度慢等缺陷,为了进一步提高花授粉算法的寻优性能,在异花授粉过程中引入t分布变异算子,提出了一种基于t分布的自适应花授粉算法(tFPA).通过7个标准测试函数进行测试比较,并对改进算法的时间复杂度进行分析,仿真结果表明,改进算法是可行有效的,其寻优速度、寻优精度以及鲁棒性均有很大程度的改善.
The flower pollination algorithm is a new heuristic optimization algorithm Which is easy to fall into the local optimum and whose convergence rate is slow. In order to improve the performance of the flower pollination algorithm, the t-distribution mutation operator is introduced in the process of cross-pollination,and the flower pollination algorithm(tFPA)based on t distribution is proposed. The improved algorithm in this paper is tested by 7 standard test functions, and the time complexity of the improved algorithm is analyzed.The simulation results show that the improved algorithm is feasible and effective, and its optimization speed, optimization accuracy, and robustness are all largely improve.
The flower pollination algorithm is a new heuristic optimization algorithm Which is easy to fall into the local optimum and whose convergence rate is slow. In order to improve the performance of the flower pollination algorithm, the t-distribution mutation operator is introduced in the process of cross-pollination,and the flower pollination algorithm(tFPA)based on t distribution is proposed. The improved algorithm in this paper is tested by 7 standard test functions, and the time complexity of the improved algorithm is analyzed.The simulation results show that the improved algorithm is feasible and effective, and its optimization speed, optimization accuracy, and robustness are all largely improve.
引文
[1] Colorni ADorigo M,Maniezzo V,et al.Distributed optimization by ant colonies[A]. Proceedings of European Conference on ArtificialLife.Paris, 1991:134-142.
[2] Kennedy J Eberhart R. Particle swarm optimization[C]IEEE International Conference on Neural Networks,Perth, 1995:1942-1948
[3] Yang Xinshe,Deb S.Cuckoo search via Levy flights[C]//Proc of World Congress on Nature&Biologically Inspired Computing.Pisca-taway:IEEE Press, 2009:210-214.
[4] Eusuff M M, Lansey K E. Optimization of water distribution network design using the shuffled frog leaping algorithm[J]. J of Water Resources Planning and Management, 2003, 129(3):210-225.
[5]冯翔,张进文,虞慧群.仿生蚊子追踪算法[J].计算机学报,2014, 37(8):1794-1808.
[6]乔莹莹,宋威,马伟.基于GA优化QPSO算法的文本聚类[J].计算机应用研究,2014, 31(10):2912-2915.
[7]马雪丽,曹德弼,刘晓冰.面向柔性工艺的作业车间调度问题混合遗传算法[J].计算机应用研究,2014,31(5):1353-1357.
[8] Yang Xinshe. Flower pollination algorithm for global optimization[C]//LNCS 7445:Proceedings of the 11th Internation Conference on Unconventional Computation and Natural Computation,Orlean, France, Sep 3-7, 2012. Berlin, Heidelberg:Springer, 2012:240-249.
[9] Yang Xinshe, Mehmet K, He Xingshe. Multi-objective flower algorithm for optimization[J]. Procedia Computer Science,2013 18(1):861-868.
[10] Sharawi M, Emary E, Imane A, et al. Flower Pollination Optimization Algorithm for Wireless Sensor Network Lifetime Global Optimization[J]. International Journal of Soft Computing and Engineering20144(3):54-59
[11] Dubey H M, Pandit M, Panigrahi B K. Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch[J]. Renewable Energy, 2015, 83:188-202.
[12]焦庆龙,徐达,李闯.基于花朵授粉算法的产品拆卸序列规划[J].计算机集成制造系统,2016, 22(12):2791-2799.
[13] Yang Xinshe.A new metaheuristic bat-inspired algorithm[M]//Gonzalez J R,Pelta D A.Nature Inspired Cooperative Strategies for Optimization. Berlin:Springer, 2010:65-74.
[14]肖辉辉.基于单纯形法和自适应步长的花朵授粉算法[J].计算机工程与科学,2016, 38(10):2126-2133.
[15] Lenin K, Reddy B R, Kalavathi M S. Shrinkage of active power loss by hybridization of flower pollination algorithm with chaotic harmony search algorithm[J]. Control Theoryand Informatics,2014, 4(8):31-38.
[16] Abdel-Raouf O, Abdel-Baset M, El-Henawy I. A new hy-brid flower pollination algorithm for solving constrained global optimization problems[J]. International Journal of Applied Operational Research, 2014, 4(2):1-13.
[17]王凤鹤,徐希莲,陈强等.北京蜜蜂授粉工作研究进展[J].中国蜂业,2012(25):28-30.
[18]王梓坤.概率论基础及其应用[M].科学出版社,1979.
[19]游鸽,刘向,万小萍.国际仿生计算研究的文献生产力与特征分析[J].华中师范大学学报(自然科学版),2016, 50(05):713-720.
[2] Kennedy J Eberhart R. Particle swarm optimization[C]IEEE International Conference on Neural Networks,Perth, 1995:1942-1948
[3] Yang Xinshe,Deb S.Cuckoo search via Levy flights[C]//Proc of World Congress on Nature&Biologically Inspired Computing.Pisca-taway:IEEE Press, 2009:210-214.
[4] Eusuff M M, Lansey K E. Optimization of water distribution network design using the shuffled frog leaping algorithm[J]. J of Water Resources Planning and Management, 2003, 129(3):210-225.
[5]冯翔,张进文,虞慧群.仿生蚊子追踪算法[J].计算机学报,2014, 37(8):1794-1808.
[6]乔莹莹,宋威,马伟.基于GA优化QPSO算法的文本聚类[J].计算机应用研究,2014, 31(10):2912-2915.
[7]马雪丽,曹德弼,刘晓冰.面向柔性工艺的作业车间调度问题混合遗传算法[J].计算机应用研究,2014,31(5):1353-1357.
[8] Yang Xinshe. Flower pollination algorithm for global optimization[C]//LNCS 7445:Proceedings of the 11th Internation Conference on Unconventional Computation and Natural Computation,Orlean, France, Sep 3-7, 2012. Berlin, Heidelberg:Springer, 2012:240-249.
[9] Yang Xinshe, Mehmet K, He Xingshe. Multi-objective flower algorithm for optimization[J]. Procedia Computer Science,2013 18(1):861-868.
[10] Sharawi M, Emary E, Imane A, et al. Flower Pollination Optimization Algorithm for Wireless Sensor Network Lifetime Global Optimization[J]. International Journal of Soft Computing and Engineering20144(3):54-59
[11] Dubey H M, Pandit M, Panigrahi B K. Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch[J]. Renewable Energy, 2015, 83:188-202.
[12]焦庆龙,徐达,李闯.基于花朵授粉算法的产品拆卸序列规划[J].计算机集成制造系统,2016, 22(12):2791-2799.
[13] Yang Xinshe.A new metaheuristic bat-inspired algorithm[M]//Gonzalez J R,Pelta D A.Nature Inspired Cooperative Strategies for Optimization. Berlin:Springer, 2010:65-74.
[14]肖辉辉.基于单纯形法和自适应步长的花朵授粉算法[J].计算机工程与科学,2016, 38(10):2126-2133.
[15] Lenin K, Reddy B R, Kalavathi M S. Shrinkage of active power loss by hybridization of flower pollination algorithm with chaotic harmony search algorithm[J]. Control Theoryand Informatics,2014, 4(8):31-38.
[16] Abdel-Raouf O, Abdel-Baset M, El-Henawy I. A new hy-brid flower pollination algorithm for solving constrained global optimization problems[J]. International Journal of Applied Operational Research, 2014, 4(2):1-13.
[17]王凤鹤,徐希莲,陈强等.北京蜜蜂授粉工作研究进展[J].中国蜂业,2012(25):28-30.
[18]王梓坤.概率论基础及其应用[M].科学出版社,1979.
[19]游鸽,刘向,万小萍.国际仿生计算研究的文献生产力与特征分析[J].华中师范大学学报(自然科学版),2016, 50(05):713-720.