共查询到16条相似文献,搜索用时 140 毫秒
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为了提升粒子群算法的全局寻优与局部精细搜索能力并加快收敛速度,提出了基于直觉模糊熵的混合粒子群优化算法.该算法采用粒子的历史最优解信息构造直觉模糊熵的自适应函数,并将熵值作为扰动因子动态调节惯性权重,同时建立自适应全局最优粒子学习策略对扰动后的粒子进行训练,在保持多样性传播的基础上选择学习对象,使粒子探索更多新区域,实现种群间的协作与并行进化.通过仿真实验,将本文算法与两种衍生算法以及其他改进粒子群算法在11个测试函数上进行比较,结果表明,本算法在求解精度、收敛速度和寻优效率上均有更好表现. 相似文献
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针对本质粒子群(BBPSO)算法存在易陷入局部最优以及过早收敛的缺点,提出了一种基于小波变异(WM)BBPSO(WMBBPSO)和模糊熵的图像分割算法,利用WMBBPSO搜索使图像模糊熵最大的参数值,得到模糊参数的最优组合,进而确定图像的分割阈值。通过与其它两种BBPSO算法的分割结果比较表明,该算法取得了令人满意的分割结果,算法运算时间较小,能够满足对煤尘浓度实时精确测量的要求。 相似文献
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为了平衡粒子群算法多样性与收敛速度,本文在Memetic框架下结合多属性决策,提出基于直觉模糊Memetic双种群混合优化算法.算法采用探索与开发分布式策略,在探索阶段,设计了社会强化算子和碰撞反弹算子提升种群多样性与勘探更多新区域;通过建立直觉模糊多属性决策对探索区域综合评估并生成可能存在的全局最优解区域,进而指导具有拉马克学习的开发种群进行局部精细搜索,实现不同策略下种群间的分布式协作与计算资源的合理分配.通过与其它5种新型进化算法在23个基准函数测试结果中体现出本算法具有更好的综合优化能力. 相似文献
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为有效地改善差分进化粒子群算法的性能,结合反向学习策略和信息交互机制,提出了一种新的混沌差分粒子群协同优化算法.该算法采用反向学习策略产生初始种群,使得初始个体尽可能均匀分布,然后将初始种群随机等分为双种群,对双种群分别采用改进的混沌差分进化算法和混沌粒子群优化算法进行协同寻优,并在双种群中引入信息交互学习机制,在维持种群多样性的同时加快收敛速度.通过对四个复杂高维的标准函数寻优测试,仿真结果表明,该算法能有效避免早熟收敛,收敛速度快,寻优精度较高,具有良好的全局搜索能力,鲁棒性好. 相似文献
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针对粒子群算法逃离局部最优能力差、易早熟收敛、求解精度低等缺点,提出了一种具有多尺度选择性学习和探测-收缩机制的PSO 算法.在多尺度选择性学习机制中,粒子根据其自身进化状态在拓扑结构、邻居个体、目标变量维等多个尺度上进行选择性学习,提升粒子个体的学习效率;在探测-收缩机制中,算法利用历史信息指导种群最优解进行探测,提高其逃离局部最优的能力,当判断种群历史最优解处于全局最优解附近时,执行空间收缩策略,将种群的搜索空间限定在较小的一个区域,增强算法的开采能力,提高算法的求解精度.通过和其它PSO算法在22个典型测试函数的实验对比表明,本算法能有效克服早熟收敛、加快收敛速度、提高求解精度. 相似文献
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《电子学报:英文版》2016,(6):1079-1088
Particle swarm optimization (PSO) has shown a good performance on solving global optimization problems.Traditional PSO has two main drawbacks of premature convergence and low convergence speed,especially on complex problems.This paper presents a new approach called Adaptive multi-layer particle swarm optimization with neighborhood search (AMPSONS),where the traditional PSO is improved by employing an adaptive multi-layer search and neighborhood search strategy to achieve a trade-off between exploitation and exploration abilities.In order to evaluate the performance of the proposed AMPSONS algorithm,the performance of AMPSONS is compared with five other PSO family algorithms,namely,CLPSO,DNLPSO,DNSPSO,global MLPSO and local MLPSO on a set of benchmark functions.The comparison results show that AMPSONS has a promising performance on majority of the test functions. 相似文献
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Da-Qing Guo Yong-Jin Zhao Hui Xiong Xiao Li 《中国电子科技》2007,5(2):149-152
A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique (LDIW) and the mutative scale chaos optimization algorithm (MSCOA) are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence. 相似文献
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Aiming at the disadvantages of Bayesian network structure learned by heuristic algorithms,which were trapping in local minimums and having low search efficiency,a method of learning Bayesian network structure based on hybrid binary slap swarm-differential evolution algorithm was proposed.An adaptive scale factor was used to balance local and global search in the swarm grouping stage.The improved mutation operator and crossover operator were taken into salp search strategy and differential search strategy respectively to renew different subswarms in the update stage.Two-point mutation operator was adopted to improve the swarm’s diversity in the stage of merging of subswarms.The convergence analysis of the proposed algorithm demonstrates that best structure can be found through the iterative search of population.Experimental results show that the convergence accuracy and efficiency of the proposed algorithm are improved compared with other algorithms. 相似文献
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《AEUE-International Journal of Electronics and Communications》2014,68(5):369-378
In this paper a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) technique is applied to the infinite impulse response (IIR) system identification problem. A modified version of PSO, called CRPSO adopts a number of random variables for having better and faster exploration and exploitation in multidimensional search space. Incorporation of craziness factor in the basic velocity expression of PSO not only brings diversity in particles but also ensures convergence to optimal solution. The proposed CRPSO based system identification approach has alleviated from the inherent drawbacks of premature convergence and stagnation, unlike real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters. 相似文献
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为了提高多目标粒子群算法优化解的多样性和收敛性,提出了一种基于多样性信息和收敛度的多目标粒子群优化算法(Multiobjective Particle Swarm Optimization based on the Diversity Information and Convergence Degree,dicdMOPSO).首先,利用非支配解多样性信息评估知识库中最优解的分布状态,设计出一种全局最优解选择机制,平衡了种群的进化过程,提高了非支配解的多样性和收敛性;其次,基于种群多样性信息设计出一种飞行参数调整机制,增强了粒子的全局探索能力和局部开发能力,获得了多样性和收敛性较好的种群.最后,将dicdMOPSO应用于标准测试函数测试,实验结果表明,dicdMOPSO与其他多目标算法相比不仅获得了多样性较高的可行解,而且能够较快的收敛到Pareto前沿. 相似文献