共查询到19条相似文献,搜索用时 187 毫秒
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为了解决采用遗传算法解析最优路径中存在的转折点较多、易陷入局部最优解、迭代次数较多以及寻优时间过长等问题,引入自适应交叉算子和变异算子,将改进后的跳点搜索(jump point search)算法与改进遗传算法融合,得到跳点搜索-遗传(jump point search-genetic,JPSG)算法。JPSG算法利用JPS算法的高效局部搜索能力来提高整体搜索能力,加速算法整体收敛趋势;利用改进遗传算法的全局搜索能力改变JPS算法不能在复杂障碍物状况下解析最优路径的状态,提高算法对动态环境的适应性。在栅格矩阵中的路径规划仿真表明,相比于改进遗传算法、传统遗传算法,JPSG算法可以有效缩短寻优执行时间,提高寻优准确率,减少运算执行次数,在稳定性、准确性、快速性上具有明显的优势。 相似文献
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提出一种基于遗传算法的进化类图像分割方法。遗传算法是一种全局搜索的算法,但是它在解决多峰复杂问题的时候会出现局部收敛的现象,出现这个现象的主要原因在于在搜索空间中群体多样性的降低导致了搜索的停滞。基于这个原因,提出一种改进的遗传算法,改进的方法通过控制遗传算法的变异概率来平衡群体的多样性程度,改进后的方法能够在解决多峰复杂问题中较多的搜索到全局解的区域。通过将改进的算法应用于图像分割的实例验证了改进算法的有效性以及算法在收敛速度及求解成功率上的优势。 相似文献
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1引言 目前的盲信号分离(Blind Source Separation)方法大多是建立在一定的神经网络模型基础上的.遗传算法作为一种并行、全局的最优搜索算法,可以为分离网络连接权值的训练提供快速性和全局性保证.本文提出了遗传算法的一种改进形式,在个体进化过程中引入基于信息理论的自然梯度学习规则,旨在加强遗传算法对局部最优解的搜索能力.对实录语音信号的分离仿真实验表明了该改进算法可以有效地提高收敛速度. 相似文献
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一种改进的广义遗传算法及其在结构动力优化问题中的应用 总被引:1,自引:0,他引:1
该文提出了一种改进的广义遗传算法。算法中引入了异种机制以提高种群的多样性,在保证收敛速度的同时防止早熟收敛。该方法应用于随机风载荷作用下有应力约束的多参数结构动力响应优化问题,数值算例表明:异种机制能够有效地提高广义遗传算法收敛于全局最优解的概率并加快收敛速度;带有异种机制的广义遗传算法能够有效地求解复杂的结构动力优化问题。 相似文献
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《中国新技术新产品》2016,(7)
从数学角度分析,配电网无功优化是一个非线性、多变量、多约束的混合规划问题。粒子群优化搜索算法被广泛应用于求解配电网无功优化问题。由于粒子群算法粒子群在进化过程易趋向同一化,失去多样性,从而使算法陷入局部最优解。本文在分析配电网无功优化的特性基础上,提出一种改进的紧融合禁忌搜索-粒子群算法用于配电网无功优化问题的求解。通过将禁忌搜索功能融合到粒子历史最优解和全局最优解寻优过程中,避免了粒子群算法寻优过程中出现的局部最优问题,从而提高粒子群算法的全局搜索能力。通过IEEE14节点系统的仿真计算结果表明,改进的算法能取得良好的效果。 相似文献
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桁架结构优化设计的遗传算法 总被引:3,自引:0,他引:3
本文提出了桁架结构系统优化设计的新方法遗传算法,它不同于常规优化算法的特点在于,从多个初始点开始寻优,并采用交迭和变异算子避免过早地收敛到局部最优解,可获得全局最优解,且不受初始值影响。 相似文献
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基于混合粒子群算法的物流配送路径优化问题研究 总被引:7,自引:3,他引:4
针对物流配送路径优化问题,提出了一种融合Powell局部寻优算法和模拟退火算法的混合粒子群算法,以克服单用粒子群算法求解问题早熟收敛的不足,增加算法的开发能力,提高算法的全局搜索能力,并进行了实验计算.计算结果表明,用混合粒子群算法求解物流配送路径优化问题,可以在一定程度上提高粒子群算法在局部搜索能力和搜索全局最优解概率,从而得到质量较高的解. 相似文献
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Whale optimization algorithm (WOA) is a new population-based metaheuristic
algorithm. WOA uses shrinking encircling mechanism, spiral rise, and random
learning strategies to update whale’s positions. WOA has merit in terms of simple
calculation and high computational accuracy, but its convergence speed is slow and it is
easy to fall into the local optimal solution. In order to overcome the shortcomings, this
paper integrates adaptive neighborhood and hybrid mutation strategies into whale
optimization algorithms, designs the average distance from itself to other whales as an
adaptive neighborhood radius, and chooses to learn from the optimal solution in the
neighborhood instead of random learning strategies. The hybrid mutation strategy is used
to enhance the ability of algorithm to jump out of the local optimal solution. A new whale
optimization algorithm (HMNWOA) is proposed. The proposed algorithm inherits the
global search capability of the original algorithm, enhances the exploitation ability,
improves the quality of the population, and thus improves the convergence speed of the
algorithm. A feature selection algorithm based on binary HMNWOA is proposed. Twelve
standard datasets from UCI repository test the validity of the proposed algorithm for
feature selection. The experimental results show that HMNWOA is very competitive
compared to the other six popular feature selection methods in improving the
classification accuracy and reducing the number of features, and ensures that HMNWOA
has strong search ability in the search feature space. 相似文献
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为了提高约束优化问题的求解精度和收敛速度,提出求解约束优化问题的改进布谷鸟搜索算法。首先分析了基本布谷鸟搜索算法全局搜索和局部搜索过程中的不足,对其中全局搜索和局部搜索迭代公式进行重新定义,然后以一定概率在最优解附近进行搜索。对12个标准约束优化问题和4个工程约束优化问题进行测试并与多种算法进行对比,实验结果和统计分析表明所提算法在求解约束优化问题上具有较强的优越性。 相似文献
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As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions. 相似文献
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K-均值聚类具有简单、快速的特点,因此被广泛应用于图像分割领域.但K-均值聚类容易陷入局部最优,影响图像分割效果.针对K-均值的缺点,提出一种基于随机权重粒子群优化(RWPSO)和K-均值聚类的图像分割算法RWPSOK.在算法运行初期,利用随机权重粒子群优化的全局搜索能力,避免算法陷入局部最优;在算法运行后期,利用K-均值聚类的局部搜索能力,实现算法快速收敛.实验表明:RWPSOK算法能有效地克服K-均值聚类易陷入局部最优的缺点,图像分割效果得到了明显改善;与传统粒子群与K-均值聚类混合算法(PSOK)相比,RWPSOK算法具有更好的分割效果和更高的分割效率. 相似文献
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To better regulate the speed of brushless DC motors, an improved algorithm based on the original Glowworm Swarm Optimization is proposed. The proposed algorithm solves the problems of poor robustness, slow convergence, and low accuracy exhibited by traditional PID controllers. When selecting the glowworm neighborhood set, an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution. After the glowworm’s position is updated, the league selection operator is introduced to search for the global optimal solution. Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm. The mathematical model of the brushless DC motor is established, the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm, and the speed of the brushless DC motor is adjusted. In a Simulink environment, a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor, and this simulation was compared with a traditional PID control. The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control. 相似文献
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目的 针对基本灰狼算法在函数优化过程中精度低、收敛速度慢、局部搜索能力差等问题,提出一种基于收敛因子和权重动态变化的自适应灰狼优化算法。方法 为了平衡算法的全局和局部搜索能力,引入聚焦距离变化率来动态调整收敛因子;使用自适应权重因子来改变算法的位置更新公式,以提高算法的收敛速度和精度。结果 仿真实验结果表明,改进后的算法在收敛精度和速度上都有了显著的提升,并且克服了灰狼算法在处理多峰函数时易陷入局部最优的缺点;对于纸浆浓度控制系统,控制效果更加理想。结论 通过改进的灰狼算法对PID控制器参数进行整定,可以显著提高系统的控制精度和其他性能指标,能更好地满足实际应用的要求。 相似文献
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针对传统算法在多根非线性方程组求解时依赖初始值的选定,求解个数不完全,求解精度不高的问题,提出了一种结合探路者算法的灰狼优化算法 (PGWO)。由于灰狼优化算法存在后期收敛速度慢等问题,结合了探路者算法,根据探路者中跟随者的更新机制对灰狼个体的位置进行改变,进而平衡算法的全局搜索和局部搜索能力。通过 9 组多根非线性方程组的仿真实验结果和其他群智能算法进行比较,实验结果表明 PGWO 算法提高了多根非线性方程组求解的精度,在求解个数上得到明显提升,进而说明了算法的有效性。 相似文献
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We propose a problem space genetic algorithm to solve single machine total weighted tardiness scheduling problems. The proposed algorithm utilizes global and time-dependent local dominance rules to improve the neighborhood structure of the search space. They are also a powerful exploitation (intensifying) tool since the global optimum is one of the local optimum solutions. Furthermore, the problem space search method significantly enhances the exploration (diversification) capability of the genetic algorithm. In summary, we can improve both solution quality and robustness over the other local search algorithms reported in the literature. 相似文献