基于动态压力控制算子的磷虾群算法
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摘要
针对基础磷虾群(KH)算法在求解复杂函数优化问题时局部搜索能力差、求解精度低、收敛速度慢、容易陷入局部最优等问题,提出一种基于动态压力控制算子的磷虾群算法(DPCKH)。该算法将一种新的动态压力控制算子加入了标准磷虾群算法,使其处理复杂函数优化问题更有效。动态压力控制算子通过欧氏距离量化了多个不同优秀个体对目标个体的诱导效应,进而在优秀个体附近加速产生新磷虾个体,提高了磷虾个体的局部探索能力。通过比较蚁群算法(ACO)、差分进化算法(DE)、磷虾群算法(KH)、改进的磷虾群算法(KHLD)和粒子群算法(PSO),DPCKH算法在7个测试函数上的结果表明,DPCKH算法与ACO算法、DE算法、KH算法、KHLD算法和PSO算法相比有着更强的局部勘测能力,其开采能力更强。
Aiming at the problem that basic Krill Herd(KH) algorithm has poor local search ability and insufficient exploitation capacity on complex function optimization problems, a Krill Herd algorithm based on Dynamic Pressure Control operator(DPCKH) was proposed. A new dynamic pressure control operator was added to the basic krill herd algorithm, which made it more effective on complex function optimization problems. The dynamic pressure control operator quantified the induction effects of several different outstanding individuals on the target individual through Euclidean distance, accelerating the production of new krill individuals near the excellent individuals and improving the local exploration ability of krill individuals. Compared to ACO(Ant Colony Optimization) algorithm, DE algorithm, KH algorithm, KHLD(Krill Herd with Linear Decreasing step) algorithm and PSO(Particle Swarm Optimization) algorithm on 7 benchmark functions, DPCKH algorithm has stronger local exporatioin and exploitation ability.
Aiming at the problem that basic Krill Herd(KH) algorithm has poor local search ability and insufficient exploitation capacity on complex function optimization problems, a Krill Herd algorithm based on Dynamic Pressure Control operator(DPCKH) was proposed. A new dynamic pressure control operator was added to the basic krill herd algorithm, which made it more effective on complex function optimization problems. The dynamic pressure control operator quantified the induction effects of several different outstanding individuals on the target individual through Euclidean distance, accelerating the production of new krill individuals near the excellent individuals and improving the local exploration ability of krill individuals. Compared to ACO(Ant Colony Optimization) algorithm, DE algorithm, KH algorithm, KHLD(Krill Herd with Linear Decreasing step) algorithm and PSO(Particle Swarm Optimization) algorithm on 7 benchmark functions, DPCKH algorithm has stronger local exporatioin and exploitation ability.
引文
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[3]SALEEM M,di CARO G A,FAROOQ M.Swarm intelligence based routing protocol for wireless sensor networks:survey and future directions[J].Information Sciences,2010,181(20):4597-4624.
[4]QU M.Lunar soft-landing trajectory of mechanics optimization based on the improved ant colony algorithm[J].Applied Mechanics and Materials,2015,3748(721):446-449.
[5]FAYCAL H,ANIS L,ANIS S,et al.A new images segmentation method based on modified particle swarm optimization algorithm[J].International Journal of Imaging Systems and Technology,2013,23(3):265-271.
[6]PAN Q-K.An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling[J].European Journal of Operational Research,2016,250(3):702-714.
[7]肖振久,孙健,王永滨,等.基于果蝇优化算法的小波域数字水印算法[J].计算机应用,2015,35(9):2527-2530.(XIAO Z J,SUN J,WANG Y B,et al.Wavelet domain digital watermarking method based on fruit fly optimization algorithm[J].Journal of Computer Applications,2015,35(9):2527-2530.)
[8]刘宝,董明刚,敬超.改进的排序变异多目标差分进化算法[J].计算机应用,2018,38(8):2157-2163.(LIU B,DONG M G,JING C.Multi-objective differential evolution algorithm with improved ranking-based mutation[J].Journal of Computer Applications,2018,38(8):2157-2163.)
[9]GANDOMI A H,ALAVI A H.Krill herd:a new bio-inspired optimization algorithm[J].Communications in Nonlinear Science and Numerical Simulation,2012,17(12):4831-4845.
[10]HOFMANN E E,HASKELL A G E,KLINCK J M,et al.Lagrangian modelling studies of Antarctic krill(euphausia superba)swarm formation[J].ICES Journal of Marine Science,2004,61(4):617-631.
[11]WANG G-G,GANDOMI A H,ALAVI A H,et al.Hybrid krill herd algorithm with differential evolution for global numerical optimization[J].Neural Computing and Applications,2014,25(2):297-308.
[12]LI J,TANG Y,HUA C,et al.An improved krill herd algorithm:krill herd with linear decreasing step[J].Applied Mathematics and Computation,2014,234(10):356-367.
[13]WANG G-G,GUO L,GANDOMI A H,et al.Chaotic krill herd algorithm[J].Information Sciences,2014,274:17-34.
[14]WANG G-G,GANDOMI A H,ALAVI A H,et al.A hybrid method based on krill herd and quantum-behaved particle swarm optimization[J].Neural Computing and Applications,2016,27(4):989-1006.
[15]刘沛,高岳林,郭伟.基于自然选择和随机扰动的改进磷虾群算法[J].小型微型计算机系统,2017,38(8):1845-1849.(LIUP,GAO Y L,GUO W.Improved krill herd algorithm based on natural selection and random disturbance[J].Journal of Chinese Computer Systems,2017,38(8):1845-1849.)