计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 206-212.doi: 10.11896/jsjkx.181102197
徐明1,焦建军1,龙文2
XU Ming1,JIAO Jian-jun1,LONG Wen2
摘要： 针对标准正弦余弦算法(Sine Cosine Algorithm,SCA)处理全局优化问题时存在收敛速度慢、易陷入局部最优和求解精度低的缺点,文中提出了一种基于非线性转换参数和随机差分变异策略的改进正弦余弦算法(LS-SCA)。首先,设计一种基于Logistic模型的非线性转换参数策略以平衡算法的全局搜索和局部开发能力;其次,引入随机差分变异策略以增强种群的多样性与避免算法陷入局部最优;最后,将非线性转换参数和随机差分变异策略进行融合。一方面,选取12个标准测试函数进行全局寻优的仿真实验。结果表明,与其他SCA类算法和最新智能算法相比,LS-SCA在收敛精度和收敛速度指标上均能达到较优的效果。其中,随机差分变异策略对LS-SCA全局寻优能力的提升尤为明显。另一方面,利用LS-SCA优化神经网络参数解决了两类经典分类问题。实验结果表明,与传统的BP算法和其他智能算法相比,基于LS-SCA的神经网络能达到较高的分类准确率。
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| [1]ZHANG M,TIAN N,PALADE V,et al.Cellular artificial bee colony algorithm with Gaussian distribution[J].Information Scie-nces,2018,462:374-401. [2]LI J,LUO Y,LI B,et al.Differential hybrid particle swarm optimization algorithm based on different dimensional variation[J].Computer Science,2018,45(5):208-214. [3]PAROUHA R P,DAS K N.A memory based differential evolution algorithm for unconstrained optimization[J].Applied Soft Computing,2016,38:501-517. [4]MIRJALILI S.SCA:A sine cosine algorithm for solving optimization problems [J].Knowledge-Based Systems,2016,96:120-133. [5]ISSA M,HASSANIEN A E,OLIVA D,et al.ASCA-PSO:Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment [J].Expert Systems with Applications,2018,99:56-70. [6]DAS S,BHATTACHARYA A,CHAKRABOTY A K.Solution of short-term hydrothermal scheduling using sine cosine algorithm[J].Soft Computing,2018,22(19):6409-6427. [7]SINDHU R,NGADIRAN R,YACOB Y M,et al.Sine-cosine algorithm for feature selection with elitism strategy and new updating mechanism[J].Neural Computing & Applications,2017,28(10):2947-2958. [8]LONG W,WU T,LIANG X,et al.Solving high-dimensional global optimization problems using an improved sine cosine algorithm[J].Expert Systems with Applications,2019,123:108-126. [9]LI S,FANG H,LIU X.Parameter optimization of support vector regression based on sine cosine algorithm[J].Expert Systems with Applications,2018,91:63-77. [10]ATTIA A F,SEHIEMY R A E,HASANIEN H M.Optimal power flow solution in power systems using a novel sine-cosine algorithm[J].Journal of Electrical Power & Energy Systems,2018,99:331-343. [11]ELAZIZ M A,OLIVA D,XIONG S.An improved opposition-based sine cosine algorithm for global optimization[J].Expert Systems with Applications,2017,90:484-500. [12]LIU Y F,MA L P.Sine cosine algorithm with nonlinear decreasing conversion parameter[J].Computer Engineering and Applications,2017,53(2):1-5. [13]ZHANG X F,BAI Y P,HAO Y,et al.Research of improved sine cosine algorithm in function optimization[J].Journal of Chongqing University of Technology (Natural Science),2017,31(2):146-152. [14]RIZK-ALLAH R M.Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems[J].Journal of Computational Design and Engineering,2018,5(2):249-273. [15]NENAVAH H,JATOTH R K.Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking[J].Applied Soft Computing,2018,62:1019-1043. [16]HANSEN N,MÜLLER S D,KOUMOUTSAKOS P.Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation[J].Evolutionary Computation,2014,11(1):1-18. [17]CHATTERJEE A,SIARRY P.Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization[J].Computers & Operations Research,2006,33(3):859-871. [18]ZHU G,KWONG S.Gbest-guided artificial bee colony algo-rithm for numerical function optimization[J].Applied Mathematics and Computation,2010,217(7):3166-3173. [19]RAO R V,PATEL V.An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems[J].Journal of Industrial Engineering Computations,2012,3(4):535-560. [20]RAHNAMAYAN S,TIZHOOSH H R,SALAMA M M A.Opposition based differential evolution[J].IEEE Transactions on Evolutionary Computation,2008,12(1):64-79. [21]RODRÍGUEZ L,CASTILLO O,SORIA J,et al.A fuzzy hierarchical operator in the grey wolf optimizer algorithm [J].Applied Soft Computing,2017,57:315-328. [22]HU H,BAI Y,XU T.Improved whale optimization algorithm based on inertia weights and theirs applications [J].Internatio-nal Journal of Circuits,Systems and Signal Processing,2017,11:12-26. [23]ALJARAH I,FARIS H,MIRJALILI S.Optimizing connection weights in neural networks using the whale optimization algorithm[J].Soft Computing,2018,22(1):1-15. |
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