基于精英混沌搜索策略的交替正余弦算法
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摘要
正余弦算法是一种新的基于种群的随机寻优方法,利用正余弦函数使解震荡性地趋于全局最优解,其线性调整策略及较弱的局部搜索能力严重地影响了算法的性能.为了提高正弦余弦算法的计算精度,提出基于精英混沌搜索策略的交替正余弦算法.新算法采用基于对数曲线的非线性调整策略修改控制参数,利用精英个体的混沌搜索策略增强算法的开发能力,并将基于该策略的正余弦算法与反向学习算法交替执行增强算法的探索能力,降低算法的时间复杂度,提高算法的收敛速度.对23个基准测试函数进行仿真实验,与改进的正余弦算法以及最新的基于启发式的算法进行比较,深入的参数实验分析以及比较结果验证了所提出算法的有效性,统计分析证实了所提出算法的优越性.
The sine cosine algorithm(SCA) is a new population-based stochastic optimization method. It uses sine and cosine functions to fluctuate the solution run to the global optimal solution. Its linear adjustment strategy and weak local search ability seriously affect the performance of the algorithm. In order to improve the calculation accuracy of the sine cosine algorithm, an alternating sine cosine algorithm based on the elite chaotic search strategy is proposed, which uses the nonlinear adjustment strategy based on logarithmic curve to modify the control parameters, uses the elite individuals' chaotic search strategy to enhance the exploitation ability of the algorithm. The SCA based on this strategy and the opposition-based learning algorithm are alternately implemented to enhance the exploration ability, reduce the time complexity and improve the convergence speed of the algorithm. The proposed method has been tested by 23 benchmark test functions, and compared with the improved SCA and the state-of-the-art heuristic algorithm. The comprehensive parameter experiment and results analysis show the effectiveness and superiority of the proposed algorithm.
The sine cosine algorithm(SCA) is a new population-based stochastic optimization method. It uses sine and cosine functions to fluctuate the solution run to the global optimal solution. Its linear adjustment strategy and weak local search ability seriously affect the performance of the algorithm. In order to improve the calculation accuracy of the sine cosine algorithm, an alternating sine cosine algorithm based on the elite chaotic search strategy is proposed, which uses the nonlinear adjustment strategy based on logarithmic curve to modify the control parameters, uses the elite individuals' chaotic search strategy to enhance the exploitation ability of the algorithm. The SCA based on this strategy and the opposition-based learning algorithm are alternately implemented to enhance the exploration ability, reduce the time complexity and improve the convergence speed of the algorithm. The proposed method has been tested by 23 benchmark test functions, and compared with the improved SCA and the state-of-the-art heuristic algorithm. The comprehensive parameter experiment and results analysis show the effectiveness and superiority of the proposed algorithm.
引文
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[14]Mirjalili S,Lewis A.S-shaped versus V-shaped transfer functions for binary particle swarm optimization[J].Swarm&Evolutionary Computation,2013,9:1-14.
[15]Mirjalili S,Mirjalili S M,Yang X S.Binary bat algorithm[J].Neural Computing&Applications,2014,25(3/4):663-681.
[16]Wang H,Liu H,Zeng S Y.Opposition-based particle swarm algorithm with Cauchy mutation[C].IEEECongress on Evolutionary Computation.Singapore:IEEE,2007:4750-4756.
[17]Zhou J,Yao X.Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing[J].Applied Soft Computing,2017,56:179-397.
[18]Pahnehkolaei S M A,Alfi A,Sadollah A,et al.Gradient-based water cycle algorithm with evaporation rate applied to chaos suppression[J].Applied Soft Computing,2016,53:420-440.
[2]Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(7):46-61.
[3]Mirjalili S.SCA:A sine cosine algorithm for solving optimization problems[J].Knowledge-Based Systems,2016,96:120-133.
[4]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.
[5]龙文,伍铁斌.协调探索和开发能力的改进灰狼优化算法[J].控制与决策,2017,32(10):1749-1758.(Long W,Wu T B.Improved grey wolf optimization algorithm coordinating the ability of exploration and exploitation[J].Control and Decision,2017,32(10):1749-1758.)
[6]Tizhoosh H R.Opposition-based learning:A new scheme for machine intelligence[C].Proc of Int Conf on Computational Intelligence for Modeling Control and Automation.Vienna:IEEE,2005:695-701.
[7]龙文,蔡绍洪,焦建军,等.求解高维优化问题的混合灰狼优化算法[J].控制与决策,2016,31(11):1991-1997.(Long W,Cai S H,Jiao J J,et al.Hybrid grey wolf optimization algorithm for high-dimensional optimization[J].Control and Decision,2016,31(11):1991-1997.)
[8]Wang H,Wu Z,Rahnamayan S,et al.Enhancing particle swarm optimization using generalized opposition-based learning[J].Information Sciences,2011,181(20):4699-4714.
[9]Xu G,Yu G S.On convergence analysis of particle swarm optimization algorithm[J].J of Computational and Applied Mathematics,2018,333:65-73.
[10]Seif Z,Ahmadi M B.An opposition-based algorithm for function optimization[J].Engineering Applications of Artificial Intelligence,2015,37:293-306.
[11]Gandomi A H,Yun G J,Yang X S,et al.Chaos-enhanced accelerated particle swarm optimization[J].Commun Nonlinear Sci Numer Simulat,2013,18(2):327-340.
[12]Yao X,Liu Y,Lin G.Evolutionary programming made faster[J].IEEE Trans on Evolutionary Computation,1999,3(2):82-102.
[13]Digalakis J,Margaritis K.On benchmarking functions for genetic algorithms[J].Int J of Computational Mathematics,2001,77(4):481-506.
[14]Mirjalili S,Lewis A.S-shaped versus V-shaped transfer functions for binary particle swarm optimization[J].Swarm&Evolutionary Computation,2013,9:1-14.
[15]Mirjalili S,Mirjalili S M,Yang X S.Binary bat algorithm[J].Neural Computing&Applications,2014,25(3/4):663-681.
[16]Wang H,Liu H,Zeng S Y.Opposition-based particle swarm algorithm with Cauchy mutation[C].IEEECongress on Evolutionary Computation.Singapore:IEEE,2007:4750-4756.
[17]Zhou J,Yao X.Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing[J].Applied Soft Computing,2017,56:179-397.
[18]Pahnehkolaei S M A,Alfi A,Sadollah A,et al.Gradient-based water cycle algorithm with evaporation rate applied to chaos suppression[J].Applied Soft Computing,2016,53:420-440.