量子行为PSO及在成品油配送方案设计中的应用
发布时间：2019-05-16 13:06
【摘要】：论文主要研究了量子行为粒子群算法的改进措施,提出了两种改进的算法,结合成品油配送方案设计问题,将理论算法推向了实用。粒子群优化是群智能算法的典型代表,而基于量子势阱中粒子的动态行为设计的量子行为粒子群算法,其优良的性能已引起国内外学者的广泛关注,现已成为国际上的研究热点。然而该算法也隶属于群智能算法,并未摆脱所有群智能优化所固有的易于陷入早熟收敛的弊端。因此研究该算法的改进措施,在丰富群智能优化理论和拓展群智能优化应用两方面都将有重要意义。论文主要研究内容如下。第一,针对量子行为粒子群优化在迭代过程中,粒子多样性迅速下降,算法易收敛于局部最优解的问题,提出了一种基于选择策略的量子行为粒子群算法。该算法同样采用量子势阱作为寻优机制,但在势阱中心的构建方面,提出了新的建立方法。在每一步迭代中,取前K个最优个体作为候选集,采用轮盘赌策略在候选集中选择一个作为势阱中心,调整其它个体向该中心移动完成一步优化。在优化过程中使K值单调下降,以期达到探索和开发的平衡。函数极值优化的实验结果表明,该算法的优化能力比原算法有明显提高。第二,目前的量子行为粒子群算法采用实数编码,搜索能力不够理想。针对这一问题,提出一种采用量子比特编码的量子行为粒子群算法。该算法在Bloch球面建立搜索机制,用泡利矩阵建立旋转轴,用Delta势阱模型计算旋转角度,用量子比特在Bloch球面上的绕轴旋转实现搜索,用Hadamard门实现变异,以避免早熟收敛。该算法可增强对解空间的遍历性,提高收敛概率。实验结果表明该算法的优化能力优于原算法。最后,针对当前优化算法在求解成品油配送优化问题方面存在的不足,本文研究了新算法在求解成品油配送车辆路径优化问题上的工程应用。本文将提出的算法应用于求解大庆油田储运销售分公司的成品油配送车辆路径优化问题,该实例的对比结果表明,新算法的优化结果明显优于该公司的现有设计结果。同时也证明了,新算法是求解成品油配送路径优化问题的有效方法,对于今后解决类似组合优化问题具有一定的参考价值。
[Abstract]:In this paper, the improvement measures of quantum behavior particle swarm optimization algorithm are studied, and two improved algorithms are proposed. Combined with the design of product oil distribution scheme, the theoretical algorithm is put into practice. Particle swarm optimization is a typical representative of swarm intelligence algorithm, and the excellent performance of quantum behavior particle swarm optimization algorithm based on the dynamic behavior of particles in quantum potential wells has attracted extensive attention of scholars at home and abroad. Now it has become a hot research topic in the world. However, the algorithm also belongs to the swarm intelligence algorithm, and does not get rid of the inherent disadvantage of all swarm intelligence optimization, which is easy to fall into premature convergence. Therefore, it will be of great significance to study the improvement measures of the algorithm in both enriching the theory of swarm intelligence optimization and expanding the application of swarm intelligence optimization. The main research contents of this paper are as follows. Firstly, in order to solve the problem that the particle diversity decreases rapidly and the algorithm converges to the local optimal solution in the iterative process of quantum behavior particle swarm optimization, a quantum behavior particle swarm optimization algorithm based on selection strategy is proposed. The algorithm also uses quantum potential well as the optimization mechanism, but in the construction of potential well center, a new method is proposed. In each iteration, the first K optimal individuals are taken as the candidate set, and the roulette strategy is used to select one as the center of the potential well in the candidate set, and the other individuals are adjusted to move to the center to complete the one-step optimization. In the optimization process, the K value decreases monotonously in order to achieve the balance between exploration and development. The experimental results of function extremum optimization show that the optimization ability of the algorithm is obviously higher than that of the original algorithm. Secondly, the current quantum behavior particle swarm optimization algorithm uses real number coding, and the search ability is not ideal. In order to solve this problem, a quantum behavior particle swarm optimization algorithm based on quantum bit coding is proposed. In this algorithm, the search mechanism is established in Bloch sphere, the rotation axis is established by Pauli matrix, the rotation angle is calculated by Delta potential well model, the search is realized by the rotation of quantum bits around the axis of Bloch sphere, and the mutation is realized by Hadamard gate in order to avoid premature convergence. The algorithm can enhance the ergodicity of the solution space and improve the convergence probability. The experimental results show that the optimization ability of the algorithm is better than that of the original algorithm. Finally, in view of the shortcomings of the current optimization algorithm in solving the optimization problem of refined oil distribution, this paper studies the engineering application of the new algorithm in solving the problem of vehicle routing optimization of refined oil distribution. In this paper, the proposed algorithm is applied to solve the routing optimization problem of finished oil distribution vehicles in Daqing Oilfield Storage and Transportation sales Branch. The comparison results of this example show that the optimization results of the new algorithm are obviously better than the existing design results of the company. At the same time, it is proved that the new algorithm is an effective method to solve the problem of product oil distribution path optimization, which has certain reference value for solving similar combinatorial optimization problems in the future.
【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE83;TP18
本文编号：2478299
[Abstract]:In this paper, the improvement measures of quantum behavior particle swarm optimization algorithm are studied, and two improved algorithms are proposed. Combined with the design of product oil distribution scheme, the theoretical algorithm is put into practice. Particle swarm optimization is a typical representative of swarm intelligence algorithm, and the excellent performance of quantum behavior particle swarm optimization algorithm based on the dynamic behavior of particles in quantum potential wells has attracted extensive attention of scholars at home and abroad. Now it has become a hot research topic in the world. However, the algorithm also belongs to the swarm intelligence algorithm, and does not get rid of the inherent disadvantage of all swarm intelligence optimization, which is easy to fall into premature convergence. Therefore, it will be of great significance to study the improvement measures of the algorithm in both enriching the theory of swarm intelligence optimization and expanding the application of swarm intelligence optimization. The main research contents of this paper are as follows. Firstly, in order to solve the problem that the particle diversity decreases rapidly and the algorithm converges to the local optimal solution in the iterative process of quantum behavior particle swarm optimization, a quantum behavior particle swarm optimization algorithm based on selection strategy is proposed. The algorithm also uses quantum potential well as the optimization mechanism, but in the construction of potential well center, a new method is proposed. In each iteration, the first K optimal individuals are taken as the candidate set, and the roulette strategy is used to select one as the center of the potential well in the candidate set, and the other individuals are adjusted to move to the center to complete the one-step optimization. In the optimization process, the K value decreases monotonously in order to achieve the balance between exploration and development. The experimental results of function extremum optimization show that the optimization ability of the algorithm is obviously higher than that of the original algorithm. Secondly, the current quantum behavior particle swarm optimization algorithm uses real number coding, and the search ability is not ideal. In order to solve this problem, a quantum behavior particle swarm optimization algorithm based on quantum bit coding is proposed. In this algorithm, the search mechanism is established in Bloch sphere, the rotation axis is established by Pauli matrix, the rotation angle is calculated by Delta potential well model, the search is realized by the rotation of quantum bits around the axis of Bloch sphere, and the mutation is realized by Hadamard gate in order to avoid premature convergence. The algorithm can enhance the ergodicity of the solution space and improve the convergence probability. The experimental results show that the optimization ability of the algorithm is better than that of the original algorithm. Finally, in view of the shortcomings of the current optimization algorithm in solving the optimization problem of refined oil distribution, this paper studies the engineering application of the new algorithm in solving the problem of vehicle routing optimization of refined oil distribution. In this paper, the proposed algorithm is applied to solve the routing optimization problem of finished oil distribution vehicles in Daqing Oilfield Storage and Transportation sales Branch. The comparison results of this example show that the optimization results of the new algorithm are obviously better than the existing design results of the company. At the same time, it is proved that the new algorithm is an effective method to solve the problem of product oil distribution path optimization, which has certain reference value for solving similar combinatorial optimization problems in the future.
【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE83;TP18
【参考文献】
相关硕士学位论文 前1条
1 杨路燕;基于风险分析的成品油二次配送路径优化研究[D];大连海事大学;2014年
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