差分进化算法和群集蜘蛛优化算法的研究

发布时间：2018-01-17 03:34

  本文关键词：差分进化算法和群集蜘蛛优化算法的研究　出处：《安徽大学》2017年硕士论文　论文类型：学位论文

  更多相关文章： 差分进化算法 群集蜘蛛优化算法 协方差矩阵 自适应权重系数 变异策略

【摘要】：差分进化(DE)算法已经成为解决连续型数值优化问题的经典方法。本文的第一部分,把简化群优化算法的交叉策略、协方差矩阵学习策略与传统的差分进化算法结合,提出一个新的DE算法的变种,我们把它称作SCDE算法。正如我们所知,DE算法的变异策略在DE算法中占据了非常重要的位置,然而,传统的DE算法的变异策略都是用相对位置来产生候选解,在本文中尝试利用个体历史最优解的绝对位置来诱导变异产生候选解,这将大大的提高种群跳出局部最优的能力。此外,我们将算法的变异和交叉操作放在由种群的协方差矩阵的所有特征向量组成的坐标系中执行,这将使算法的交叉和变异操作具有旋转不变性。实验结果表明,本文提出的新的交叉和变异策略可以大大提高DE算法在CEC 2013中28个测试函数的结果。并且将SCDE算法应用在解决组合优化问题之TSP问题后也取得了较优的结果。群集蜘蛛优化算法是由Cuevas首次提出模拟群集蜘蛛相互协作的一种新型的群智能优化算法。从数值模拟的结果显示,相比较对比算法粒子群算法、人工蜂群算法,群集蜘蛛算法在全局寻优能力方面的性能更强。然而,平衡算法的全局搜索能力和勘探能力是对一个群智能算法至关重要的一点,它直接影响算法是否会过早收敛或精确度不足,这也是传统的群集蜘蛛优化算法所存在的问题。受到粒子群算法和差分进化算法启发,在本文的第二部分提出一种新的基于差分进化变异策略和自适应权重系数的群集蜘蛛优化算法(表示为wDESSO)。在新算法中我们主要工作有以下几点:1.一个随着种群迭代次数动态变化的权重系数将被提出,用于自适应群集优化算法的搜索范围;2.在算法结束了婚配操作之后,两种差分进化算法的变异策略将被应用在新的算法中,用于增强算法的全局搜索能力和跳出局部最优的能力。根据不同的变异策略,新提出的算法可以被分为两类:wDESSO-Ⅰ算法和wDESSO-Ⅱ算法。随后,几组实验将用来检验新的群集蜘蛛算法的性能,其中一个实验是将新型的群集蜘蛛优化算法与传统的群集蜘蛛优化算法、粒子群算法、人工蜂群算法在15个标准测试集上做比较,并对结果做了威尔科克森符号秩检验;另外一组实验是与一些提高的优化算法比较。结果表明,在解决复杂的数值问题上,基于差分进化变异策略的群集蜘蛛算法(wDESSO)的表现要明显好于其他的对比算法。
[Abstract]:Differential evolution (DED) algorithm has become a classical method for solving continuous numerical optimization problems. In the first part of this paper, the crossover strategy of simplified group optimization algorithm is proposed. The covariance matrix learning strategy is combined with the traditional differential evolution algorithm, and a new DE algorithm is proposed, which we call the SCDE algorithm, as we know it. The mutation strategy of DE algorithm occupies a very important position in DE algorithm. However, the traditional mutation strategy of DE algorithm is to generate candidate solution by using relative position. In this paper, we try to use the absolute position of individual historical optimal solution to induce mutation to produce candidate solution, which will greatly improve the ability of population to jump out of local optimum. The mutation and crossover operations of the algorithm are carried out in a coordinate system composed of all the eigenvectors of the covariance matrix of the population, which will make the crossover and mutation operations of the algorithm rotation-invariant. The new crossover and mutation strategies proposed in this paper can greatly improve the DE algorithm in CEC. The results of 28 test functions in 2013. The SCDE algorithm is applied to solve the TSP problem of combinatorial optimization problem. The cluster spider optimization algorithm is led by Cuevas. A new swarm intelligence optimization algorithm is proposed to simulate the collaboration of cluster spiders. Compared with particle swarm optimization algorithm, artificial bee swarm algorithm, swarm spider algorithm has better performance in global optimization. The global search ability and exploration ability of the balanced algorithm are very important to a swarm intelligence algorithm, which directly affects whether the algorithm will converge prematurely or not. This is also the problem of the traditional swarm spider optimization algorithm, which is inspired by particle swarm optimization and differential evolution algorithm. In the second part of this paper, a new algorithm of swarm spider optimization based on differential evolution mutation strategy and adaptive weight coefficient is proposed. In the new algorithm, we mainly work on the following points: 1.A weight coefficient which varies dynamically with the number of iterations of the population will be proposed. Search range for adaptive cluster optimization algorithm; 2. After the conclusion of the matching operation, the mutation strategies of the two differential evolution algorithms will be applied to the new algorithm. Used to enhance the global search ability of the algorithm and the ability to jump out of the local optimum. According to different mutation strategies. The proposed algorithms can be divided into two categories: wDESSO- 鈪，

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