计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 297-301.doi: 10.11896/jsjkx.190700063
张志强, 鲁晓锋, 隋连升, 李军怀
ZHANG Zhi-qiang, LU Xiao-feng, SUI Lian-sheng, LI Jun-huai
摘要: 为了提高樽海鞘群算法(Salp Swarm Algorithm, SSA)的收敛速度、计算精度和全局优化能力, 在分析总结粒子群优化(Particle Swarm Optimization, PSO)和差分进化(Differential Evolution, DE)算法相关研究成果后, 提出了一种集成PSO算法随机惯性权重和DE算法差分变异操作的改进SSA算法——iSSA。首先, 将PSO算法的随机惯性权重引入SSA算法的追随者位置更新公式中, 用于增强和平衡SSA算法的勘探与开发能力;其次, 用DE算法的变异操作替代SSA算法的领导者位置更新操作, 以提高SSA算法的收敛速度和计算精度。为了检验随机惯性权重和差分变异操作对SSA算法的改进效果, 在多个高维基准函数上进行了仿真实验, 并与其他改进SSA算法进行了比较。实验结果及分析表明, 与SSA算法和两个典型的改进SSA算法(ESSA和CASSA)相比, 集成随机惯性权重和差分变异操作的iSSA算法, 在没有增加算法时间复杂度的情况下, 显著地提高了SSA算法的收敛速度、计算精度和全局优化能力, 并且优于ESSA算法和CASSA 算法。
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[1]MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al.Salp Swarm Algorithm:A bio-inspired optimizer for engineering design problems [J].Advances in Engineering Software, 2017, 114:163-191. [2]FARIS H, MIRJALILI S, ALJARAH I, et al.Nature-Inspired Optimizers [M].Switzerland:Springer International Publishing, 2020:185-199. [3]SAYED G I, KHORIBA G, HAGGAG M H.A novel chaotic salp swarm algorithm for global optimization and feature selection [J].Applied Intelligence, 2018, 48(10):3462-3481. [4]WANG D, ZHOU Y, JIANG S, et al.A Simplex Method-Based Salp Swarm Algorithm for Numerical and Engineering Optimization [C]∥International Conference on Intelligent Information Processing.Cham:Springer, 2018:150-159. [5]QAIS M H, HASANIEN H M, ALGHUWAINEM S.Enhanced salp swarm algorithm:Application to variable speed wind gene-rators [J].Engineering Applications of Artificial Intelligence, 2019, 80:82-96. [6]POLI R, KENNEDY J, BLACKWELL T.Particle swarm optimization.An overview [J].Swarm Intelligence, 2007, 1(1):33-57. [7]WU J, NAN R, CHEN L.Improved salp swarm algorithm based on weight factor and adaptive mutation [J].Journal of Experimental & Theoretical Artificial Intelligence, 2019, 31(3):493-515. [8]MIRJALILI S.SCA:A Sine Cosine Algorithm for solving optimization problems [J].Knowledge-Based Systems, 2016, 96:120-133. [9]SINGH N, SON L H, CHICLANA F, et al.A new fusion of salp swarm with sine cosine for optimization of non-linear functions [J].Engineering with Computers, 2020, 36:185-212. [10]MAJHI S K, MISHRA A, PRADHAN R.A chaotic salp swarm algorithm based on quadratic integrate and fire neural model for function optimization [J].Progress in Artificial Intelligence, 2019:1-16. [11]YANG B, ZHONG L, ZHANG X, et al.Novel bio-inspiredmemetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition [J].Journal of Cleaner Production, 2019, 215:1203-1222. [12]WANG M Q, WANG Y, JI Z C.Permanent Magnet Synchronous Motor Multi-parameter Identification Based on Improved Salp Swarm Algorithm [J].Journal of System Simulation, 2018, 30(11):4284-4297. [13]ZHANG D M, CHEN Z Y, XIN Z Y, et al.Salp Swarm Algorithm Based on Craziness and Adaptive [J].Control and Decision, 2020, 35(9):2112-2120. [14]PRICE K, STORN R M, LAMPINEN J A.Differential Evolution:A Practical Approach to Global Optimization [M].Berlin:Springer-Verlag, 2005:37-130. [15]HARRISON K R, ENGELBRECHT A P, OMBUKI-BERMAN B M.Inertia weight control strategies for particle swarm optimization [J].Swarm Intelligence, 2016, 10(4):267-305. [16]EBERHART R C, SHI Y.Tracking and optimizing dynamic systems with particle swarms [C]∥Proceedings of the 2001 Congress on Evolutionary Computation.IEEE, 2001:94-100. [17]DAS S, MULLICK S S, SUGANTHAN P N.Recent advances in differential evolution-An updated survey [J].Swarm and Evolutionary Computation, 2016, 27:1-30. [18]MIRJALILI S.Salp Swarm Algorithm[EB/OL].http://www.alimirjalili.com/SSA.html. |
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