基于越界重置和高斯变异的蝙蝠优化算法
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摘要
针对蝙蝠算法个体越界、易早熟收敛的问题,提出一种基于越界重置和高斯变异的蝙蝠优化算法。新算法将飞越解空间边界的个体拉回解空间内,利用越界重置策略重新分配位置。通过高斯变异策略控制个体的搜索范围,使种群以最优解为中心向四周呈放射状搜索,增强了算法的局部搜索和全局寻优能力。蝙蝠算法在靠近目标解时响度和脉冲发射频率更新不协调,影响了算法的持续进化能力,通过线性渐变策略保证响度和脉冲发射频率的变化与算法持续进化相适应。研究了在解空间不同位置关系的情况下新算法和对比算法的优化能力,并结合实验数据对算法收敛稳定性进行分析。实验结果表明,提出的新算法具有较好的收敛速度和精度,其全局寻优能力和高维问题优化能力体现了很好的鲁棒性。
Aiming at the problem that individuals cross border and suffer from premature convergence in the bat algorithm,we propose an improved bat algorithm based on cross-border relocation and Gaussian mutation.The algorithm pulls the individuals which cross the solution boundary back into the solution space,and uses the cross-border relocation strategy to relocate.We then use the Gaussian mutation strategy to control the search range of individuals,and the population is radically searched around the optimal solution as the center,which enhances the local search and global optimization ability of the bat algorithm.Since the loudness and pulse frequency of the bat algorithm are inconsistent when bats approaching the target solution,which affects the continuous evolution ability of the algorithm,we introduce the linear gradient strategy to ensure that the updates of loudness and pulse frequency are compatible with the continuous evolution of the algorithm.We compare the optimization ability of the new algorithm with other algorithms under different position relationships in the solution space,and analyze the convergence stability of the new algorithm with the experimental data.Experimental results show that the proposed algorithm has better convergence speed and accuracy.In addition,the global optimization ability and high dimensional problem optimization ability of the algorithm demonstrate good robustness.
Aiming at the problem that individuals cross border and suffer from premature convergence in the bat algorithm,we propose an improved bat algorithm based on cross-border relocation and Gaussian mutation.The algorithm pulls the individuals which cross the solution boundary back into the solution space,and uses the cross-border relocation strategy to relocate.We then use the Gaussian mutation strategy to control the search range of individuals,and the population is radically searched around the optimal solution as the center,which enhances the local search and global optimization ability of the bat algorithm.Since the loudness and pulse frequency of the bat algorithm are inconsistent when bats approaching the target solution,which affects the continuous evolution ability of the algorithm,we introduce the linear gradient strategy to ensure that the updates of loudness and pulse frequency are compatible with the continuous evolution of the algorithm.We compare the optimization ability of the new algorithm with other algorithms under different position relationships in the solution space,and analyze the convergence stability of the new algorithm with the experimental data.Experimental results show that the proposed algorithm has better convergence speed and accuracy.In addition,the global optimization ability and high dimensional problem optimization ability of the algorithm demonstrate good robustness.
引文
[1]Yang X,Gandomi A H.Bat algorithm:A novel approach for global engineering optimization[J].Engineering Computations,2012,29(5):464-483.
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[4]Huang T,Mohan A S.A hybrid boundary condition for robust particle swarm optimization[J].IEEE Antennas&Wireless Propagation Letters,2005,4(1):112-117.
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[6]Chi Yu-hong,Sun Fu-chun,Wang Wei-jun,et al.An improved particle swarm optimization algorithm with search space zoomed factor and attractor[J].Chinese Journal of Computers,2011,34(1):115-130.(in Chinese)
[7]Yue Xiao-xue,Zheng Yun-shui,Lin Jun-ting.Bat algorithm with adaptive mutation[J].Computer Measurement&Control,2015,23(2):516-519.(in Chinese)
[8]Gao Shan,Ma Liang,Zhang Hui-zhen.Niche bat algorithm for function optimization[J].Mathematics in Practice and Theory,2014,44(15):253-260.(in Chinese)
[9]He Zi-kuang,Wu Wei-min,Lin Zhi-yi.Hybrid bat algorithm and its application[J].Computer Engineering and Design,2016,37(11):3112-3117.(in Chinese)
[10]Zhang Yu-nan,Liu Fu-yong.Improved variation step adaptive bat algorithm and application[J].Journal of Guangxi U-niversity for Nationalities(Natural Science Edition),2013,19(2):51-54.(in Chinese)
[6]迟玉红,孙富春,王维军,等.基于空间缩放和吸引子的粒子群优化算法[J].计算机学报,2011,34(1):115-130.
[7]岳小雪,郑云水,林俊亭.自适应变异的蝙蝠算法[J].计算机测量与控制,2015,23(2):516-519.
[8]高珊,马良,张惠珍.函数优化的小生境蝙蝠算法[J].数学的实践与认识,2014,44(15):253-260.
[9]何子旷,吴伟民,林志毅.混合蝙蝠算法及其应用[J].计算机工程与设计,2016,37(11):3112-3117.
[10]张宇楠,刘付永.一种改进的变步长自适应蝙蝠算法及其应用[J].广西民族大学学报(自然科学版),2013,19(2):51-54.
[2]Kennedy J,Eberhart R.Particle swarm optimization[C]∥Proc of IEEE International Conference on Neural Networks,1995:1942-1948.
[3]Robinson J,Rahmat-Samii Y.Particle swarm optimization in electromagnetics[J].IEEE Transactions on Antennas&Propagation,2004,52(2):397-407.
[4]Huang T,Mohan A S.A hybrid boundary condition for robust particle swarm optimization[J].IEEE Antennas&Wireless Propagation Letters,2005,4(1):112-117.
[5]Xu S,Rahmat-Samii Y.Boundary conditions in particle swarm optimization revisited[J].IEEE Transactions on Antennas&Propagation,2007,55(3):760-765.
[6]Chi Yu-hong,Sun Fu-chun,Wang Wei-jun,et al.An improved particle swarm optimization algorithm with search space zoomed factor and attractor[J].Chinese Journal of Computers,2011,34(1):115-130.(in Chinese)
[7]Yue Xiao-xue,Zheng Yun-shui,Lin Jun-ting.Bat algorithm with adaptive mutation[J].Computer Measurement&Control,2015,23(2):516-519.(in Chinese)
[8]Gao Shan,Ma Liang,Zhang Hui-zhen.Niche bat algorithm for function optimization[J].Mathematics in Practice and Theory,2014,44(15):253-260.(in Chinese)
[9]He Zi-kuang,Wu Wei-min,Lin Zhi-yi.Hybrid bat algorithm and its application[J].Computer Engineering and Design,2016,37(11):3112-3117.(in Chinese)
[10]Zhang Yu-nan,Liu Fu-yong.Improved variation step adaptive bat algorithm and application[J].Journal of Guangxi U-niversity for Nationalities(Natural Science Edition),2013,19(2):51-54.(in Chinese)
[6]迟玉红,孙富春,王维军,等.基于空间缩放和吸引子的粒子群优化算法[J].计算机学报,2011,34(1):115-130.
[7]岳小雪,郑云水,林俊亭.自适应变异的蝙蝠算法[J].计算机测量与控制,2015,23(2):516-519.
[8]高珊,马良,张惠珍.函数优化的小生境蝙蝠算法[J].数学的实践与认识,2014,44(15):253-260.
[9]何子旷,吴伟民,林志毅.混合蝙蝠算法及其应用[J].计算机工程与设计,2016,37(11):3112-3117.
[10]张宇楠,刘付永.一种改进的变步长自适应蝙蝠算法及其应用[J].广西民族大学学报(自然科学版),2013,19(2):51-54.
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