| 引用本文: | 滕志军,吕金玲,郭力文,许媛媛.一种基于Tent映射的混合灰狼优化的改进算法[J].哈尔滨工业大学学报,2018,50(11):40.DOI:10.11918/j.issn.0367-6234.201806096 |
| TENG Zhijun,Lü Jinling,GUO Liwen,XU Yuanyuan.An improved hybrid grey wolf optimization algorithm based on Tent mapping[J].Journal of Harbin Institute of Technology,2018,50(11):40.DOI:10.11918/j.issn.0367-6234.201806096 |
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| 摘要: |
| 针对基本灰狼算法易陷入局部最优、未考虑个体自身经验等问题, 本文提出一种基于Tent映射的混合灰狼优化算法(grey wolf optimization algorithm based on particle swarm optimization, 简称PSO_GWO).首先, 其通过Tent混沌映射产生初始种群, 增加种群个体的多样性; 其次, 采用非线性控制参数, 前期递减速度慢, 能够增加全局搜索能力, 避免算法陷入局部最优, 后期收敛因子递减速度快, 增加算法局部搜索能力, 从而提高整体收敛速度; 最后, 引入粒子群算法的思想, 将个体自身经历过最优值与种群最优值相结合来更新灰狼个体的位置信息, 从而保留灰狼个体自身最佳位置信息.为验证该算法的有效性, 本文借助9个标准测试函数来与其他三种算法进行对比.实验结果表明, 本文提出的算法比其他三种算法在单峰函数和多峰函数上搜索到的最优解更加理想; PSO_GWO算法比IGWO算法(the improved grey wolf optimization algorithm)在计算时间复杂度方面效果较好; 同时, 随着种群规模增大, PSO_GWO算法收敛值逐渐接近理想值.因此, 本文提出的PSO_GWO算法能更快搜索到全局最优解, 且鲁棒性更好.
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| 关键词: 灰狼优化算法 Tent映射 非线性控制参数 粒子群算法 惯性权重系数 |
| DOI：10.11918/j.issn.0367-6234.201806096 |
| 分类号:TP273 |
| 文献标识码:A |
| 基金项目:国家自然科学基金青年科学基金项目(61501107)； 吉林省教育厅“十三五”科学研究规划项目(JJKH20180439KJ) |
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| An improved hybrid grey wolf optimization algorithm based on Tent mapping |
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TENG Zhijun,Lü Jinling,GUO Liwen,XU Yuanyuan
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(School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, Jilin, China)
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| Abstract: |
| As the grey wolf algorithm is easy to fall into local optimum and lack of consideration of own experience, this paper proposes a grey wolf optimization algorithm based on particle swarm optimization (PSO_GWO).Firstly, it generates the initial population through Tent chaotic map, which increases the diversity of the population.Then, this paper adopts non-linear control parameters.Its decline speed is slow in the early stage, which can increase the global search ability and prevent the algorithm from falling into the local optimum.The decline speed is quick in the later stage, which can increase the algorithm's local search ability and improve the overall convergence speed.Finally, the idea of particle swarm optimization is introduced to update the position information of individual wolves by combining the best value of the individual with the best value of the population, so as to preserve the best position information of the wolves.In order to verify the effectiveness of the algorithm, this paper compared it with three other algorithms.The experimental results suggested that the solution searched by this paper is more ideal than the other three algorithms on the unimodal function and the multimodal function.The PSO_GWO algorithm worked better than the IGWO algorithm (the improved grey wolf optimization algorithm) in calculating the time complexity; as the population size increased, the convergence value of the PSO_GWO algorithm gradually approached the ideal value.So the proposed algorithm can quickly search the global optimal solution and has better robustness.
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| Key words: grey wolf optimization algorithm Tent chaotic map nonlinear control parameter particle swarm optimization inertia weight coefficient |
