根生群优化算法
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摘要
针对全局优化问题,基于一类支持向量数据描述(SVDD)和已有的根系生长算法提出一种新的智能优化算法——根生群优化算法,将根系划分为主根群体和侧根群体。基于SVDD描述主根群体的生长行为,将土壤中养分浓度最高的位置作为全局优化的目标,构建了根系生长模型;分析了RGSO的数学模型,从理论上证明了RGSO的收敛性。在实验中,与当前最先进的其他三种算法进行综合比较,并观察了不同参数对优化效果的影响。实验结果验证了RGSO的收敛性和有效性,表明RGSO是一种解决全局优化问题的有效算法。
Aiming at the global optimization problem,this paper proposed a new intelligent algorithm called RGSO,based on SVDD and the root algorithm existed. The root system differentiated into the taproot and the lateral root group. Inspired by this growth behavior,this paper described the growth behavior of the taproot tips based on SVDD. The algorithm constructed the root growth model,and used the point of the highest concentration of nutrients in the soil as the target of global optimization.It analyzed the mathematical model of RGSO,and proved its convergence theoretically. In the experiment,this paper compared RGSO with the other three advanced algorithms,and tested the optimization effect with different parameters. The result of the experiment verified the convergence and effectiveness of RGSO,and it indicates that RGSO is an effective algorithm to solve global optimization problem.
Aiming at the global optimization problem,this paper proposed a new intelligent algorithm called RGSO,based on SVDD and the root algorithm existed. The root system differentiated into the taproot and the lateral root group. Inspired by this growth behavior,this paper described the growth behavior of the taproot tips based on SVDD. The algorithm constructed the root growth model,and used the point of the highest concentration of nutrients in the soil as the target of global optimization.It analyzed the mathematical model of RGSO,and proved its convergence theoretically. In the experiment,this paper compared RGSO with the other three advanced algorithms,and tested the optimization effect with different parameters. The result of the experiment verified the convergence and effectiveness of RGSO,and it indicates that RGSO is an effective algorithm to solve global optimization problem.
引文
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[3] Ziebert F,Aranson I S. Computational approaches to substrate-based cell motility[J]. Computational Materials,2016,2016(2):article ID 16019.
[4] Cuevas E,EchavarraíA,Ramríez-Ortegón M A. An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation[J]. Applied Intelligence,2014,40(2):256-272.
[5] Cuevas E,Cienfuegos M,Zaldv ar D,et al. A swarm optimization algorithm inspired in the behavior of the social-spider[J]. Expert Systems with Applications,2014,40(16):6374-6384.
[6] Rashid M A,Khatib F,Md Hoque T,et al. An enhanced genetic algorithm for ab initio protein structure prediction[J]. IEEE Trans on Evolutionary Computation,2016,20(4):627-644.
[7] Zhu Qun,Yin Zhonggang,Zhang Yanqing,et al. Research on twodegree-of-freedom internal model control strategy for induction motor based on immune algorithm[J]. IEEE Trans on Industrial Electronics,2016,63(3):1981-1992.
[8] Gao Yang,Du Wenbo,Yan Gang. Selectively-informed particle swarm optimization[J]. Scientific Reports,2015,2015(5):article ID 9295.
[9] Kennedy J,Eberhart R C. Particle swarm optimization[C]//Proc of IEEE International Conference on Neural Networks. Piscataway,NJ:IEEE Press,1995:1942-1948.
[10]Zhang Hao,Zhu Yunlong,Chen Hanning. Root growth model:a novel approach to numerical function optimization and simulation of plant root system[J]. Soft Computing,2014,18(3):521-537.
[11] Niazmardi S,Homayouni S,Safari A. An improved FCM algorithm based on the SVDD for unsupervised hyperspectral data classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2013,6(2):831-839.
[12]Tisan A,Chin J. An end-user platform for FPGA-based design and rapid prototyping of feedforward artificial neural networks with on-chip backpropagation learning[J]. IEEE Trans on Industrial Informatics,2016,12(3):1124-1133.
[13] Ghani H A,Hamzah M H,Syahali S,et al. Ant-colony algorithm with interference cancellation for cooperative transmission[J]. IET SignalProcessing,2016,10(6):603-610.
[14]Daneshmand S V,Heydari H. A diversified multiobjective simulated annealing and genetic algorithm for optimizing a three-phase HTS transformer[J]. IEEE Trans on Applied Superconductivity,2016,26(2):1-10.
[15]Ahmed F,Deb K. Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms[J]. Soft Computing,2013,17(7):1283-1299.
[16] Zhang Zhuhong,Qian Shuqu. Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems[J]. Soft Computing,2011,15(7):1333-1349.
[17]Li Changhe,Yang Shengxiang,Nguyen T T. A self-learning particle swarm optimizer for global optimization problems[J]. IEEE Trans on Systems,Man,and Cybernetics,Part B:Cybernetics,2012,42(3):627-646.
[18]Gong Yuejiao,Zhang Jun,Chung H S H,et al. An efficient resource allocation scheme using particle swarm optimization[J]. IEEE Trans on Evolutionary Computation,2012,16(6):801-816.
[19]Klein C E,Segundo E H V,Mariani V C,et al. Modified social-spider optimization algorithm applied to electromagnetic optimization[J].IEEE Tran on Magnetics,2016,52(3):1-4.
[20]Liu Liang,Song Yuning,Zhang Haiyang,et al. Physarum optimization:a biology-inspired algorithm for the steiner tree problem in networks[J]. IEEE Trans on Computers,2015,64(3):818-831.
[21]Feng Xiang,Lau F C M,Yu Huiqun. A novel bio-inspired approach based on the behavior of mosquitoes[J]. Information Sciences,2013,233(6):87-108.