求解TSP问题的一种改进遗传算法

TSP问题是典型的NP完全问题,遗传算法是基于生物进化论的一种全局优化搜索算法。文中针对TSP问题传统遗传算法的缺点,提出了一种改进的遗传算法,并且给出选择、交叉和变异操作的设计。最后,以该算法求解中国旅行商问题(C-TSP)为例,表明该改进遗传算法具有更好的收敛性,可以得到更好的最优解。     

基于遗传算法的TSP问题优化方法

针对传统遗传算法在巡回商旅问题优化计算中存在的弊端——收敛速度慢,迭代次数多。在传统遗传算法基础上,设计出一种加入人工选择和定向突变的优化改进算法。该优化算法通过人工方法保存具有有利变异个体和淘汰具有不利变异个体,有利变异个体进行杂交和变异,从而提高遗传算法的收敛速度,减少遗传算法的迭代次数。同时针对遗传算法易陷入局部最优解的情况,在优化算法中引入自适应参数算法,针对遗传算法的不同阶段,实现杂交概率和变异概率的自适应调节,防止算法陷入局部最优解。最后,采用国际标准的TSP测试集(TSPLIB)对优化算法的优良性进行验证,实验表明,对比其他算法,该优化算法在TSP最优解的质量上提高10%左右。     

改进的遗传算法在TSP中的应用

分析了遗传算法和模拟算法的主要优缺点,提出一种用于求解旅行商问题（TSP）的改进遗传算法,该算法有效地将遗传算法和模拟退火算法相结合,在很大程度上缩短了算法的搜索时间;利用MATLAB对多种TSP问题进行仿真研究,实验结果证明了改进的遗传算法的有效性。     

混合遗传蚁群算法的改进及在TSP问题中的应用研究

蚁群算法(ACA)与遗传算法(GA)都属于仿生型优化算法,是解决组合优化问题的强有力工具,并都分别成功应用于旅行商问题(TSP)中.本文将两种算法进行融合,并给出了新的融合方式.实验结果表明,新的遗传蚁群混合算法有效地改进了算法的全局收敛性,并加快了收敛速度.     

面向TSP问题的免疫遗传算法研究

TSP问题作为NP难题的典型代表,计算机算法理论研究的热点,各种针对该问题的算法层出不穷。对近期出现的面向TSP问题的免疫遗传算法进行了介绍与总结,在分析了算法特点之后,提出了算法的改进方向,对TSP问题的研究进行了展望。     

基于求解TSP问题的ACA-GA-PSO算法

为了有效求解TSP问题,提出一种融合蚁群算法、遗传算法、粒子群优化算法思想的混合算法。该算法基于最大-最小蚁群系统框架,在选择下一个城市时采用局部搜索策略避免陷入局部最优,在每次循环结束时用演化交叉策略优化得到的全局最短路径,从而提高求解TSP问题的求解精度及收敛速度。TSPLIB中不同规模的TSP问题的仿真实验结果表明了该算法的有效性与可行性。     

TSP问题解决的遗传算法实现

TSP是一个典型的组合优化问题,并且是一个NP难题,其可能的路径总数与城市数目n是成指数型增长的,所以一般很难精确地求出其最优解,因而寻找出有效的近似求解算法就具有重要的意义。文章首先对TSP问题和遗传算法基本原理进行了简单介绍,然后采用贪婪交叉算子和贪婪倒位变异算子改进基本遗传算法,详细给出了该算法的基本实现步骤和流程图．并且采用该算法对TSPLIB中3个典型问题进行了仿真实验,实验结果很理想。     

一种改进的并行混合遗传算法在求解TSP问题中的应用

遗传算法(GeneticAlgorithm,GA)是一种基于自然群体遗传机制的有效搜索算法。由于它在搜索空间中同时考虑许多点,这样就减少了收敛于局部极小的可能,也增加了处理的并行性。因此,可以利用并行遗传算法(PGA)研究典型的组合优化实例-TSP问题(旅行商问题)的求解问题,提出一种改进的主从式并行混合遗传算法求解TSP问题。实验结果表明,该方法在解的精度和速度上优于以前的算法。     

改进的TSP算法求解供应链一对多配送问题

TSP问题即旅游最优路线问题,是数学领域中著名问题之一.如今,把TSP用于解决物流行业中运输线路优化已成为一种新的趋向.针对TSP问题没有一种简便、统一的求解方法,提出了改进的TSP算法,即把问题转化为求解最小树和图中悬挂点的匹配问题,从而大大缩小了TSP问题解的搜索空间,降低了求解难度,得到一种改进的求解方法,解决了供应链一对多配送问题.     

一种优化的蚁群算法

蚁群算法是一种新型的启发式算法,它具有许多优良性质,被广泛用于求解组合优化问题,但基本蚁群算法也存在诸多不足。为使蚊群算法对应TSP问题的解更加优良,提出了一种改进的蚁群算法并对它进行了试验,结果表明改进算法是有效的,这也为蚁群算法的优化提供了一个新的途径。     

企业自主创新项目风险评价模型与实证研究

针对企业自主创新项目风险评价中的高维、非线性问题,提出了一种基于主成分分析和遗传神经网络的企业自主创新项目风险评价方法.该方法利用主成分分析对企业自主创新项目风险评价体系进行特征提取,利用遗传算法直接训练神经网络的权重形成遗传神经网络,特征提取后的综合主成分指标进入遗传神经网络的智能评价系统.实证结果表明,该方法具有较好的泛化能力,与标准BP神经网络方法相比,该方法具有明显的优势.     

Optimized scheduling of resource-constraints in projects for smart construction

In real-life applications, resources in construction projects are always limited. It is of great practical importance to shorten the project duration by using intelligent models (i.e., evolutionary computations such as genetic algorithm (GA) and particle swarm optimization (PSO) to make the construction process reasonable considering the limited resources. However, in the general EC-based model, for example, PSO easily falls into a local optimum when solving the problem of limited resources and the shortest period in scheduling a large network. This paper proposes two PSO-based models, which are resource-constrained adaptive particle swarm optimization (RC-APSO) and an input-adaptive particle swarm optimization (iRC-APSO) to respectively solve the static and dynamic situations of resource-constraint problems. The RC-APSO uses adaptive heuristic particle swarm optimization (AHPSO) to solve the limited resource and shortest duration problem based on the analysis of the constraints of process resources, time limits, and logic. The iRC-APSO method is a combination of AHPSO and network scheduling and is used to solve the proposed dynamic resource minimum duration problem model. From the experimental results, the probability of obtaining the shortest duration of the RC-APSO is higher than that of the genetic PSO and GA models, and the accuracy and stability of the algorithm are significantly improved compared with the other two algorithms, providing a new method for solving the resource-constrained shortest duration problem. In addition, the computational results show that iRC-APSO can obtain the shortest time constraint and the design scheme after each delay, which is more valuable than the static problem for practical project planning.     

Recombination, rationality, reductionism and romantic reactions: culture, computers, and the genetic algorithm

The genetic algorithm (GA) is a computational procedure that 'evolves' solutions to optimization problems by generating populations of possible solutions, and then by treating these solutions metaphorically as individuals that can 'mate' and 'compete' to 'survive' and 'reproduce'. In this paper, I explore how culturally specific notions of evolution, population, reproduction, sex/gender, and kinship inflect the ways GAs are assembled and understood. Combining the results of fieldwork among GA workers with analysis of GA texts, I contend that the picture of 'nature' embedded in GAs is resonant with the values of secularized Judeo-Christian white middle-class US-American and European heterosexual culture. I also maintain that GA formulations are accented by languages inherited from sociobiology. I argue that examining GAs can help us track how dominant meanings of 'nature' are being stabilized and refigured in an age in which exchanges of metaphor between biology and computer science are increasingly common.     

一种改进的基于遗传算法的人脸识别方法

针对常见的基于PCA的人脸识别方法在识别过程中所遇到的计算量大、分类特征不佳等问题,提出了基于遗传算法的PCA+2DPCA的人脸识别方法,并通过实验,利用ORL人脸数据库验证了该方法的可行性。     

电网故障诊断的二进制人工鱼群算法

电网故障诊断的基本思想是根据保护动作原理将故障诊断问题表示为0-1规划问题。为了保证电网故障诊断的准确性和实时性,提出了一种改进的人工鱼群算法——二进制人工鱼群算法。分析了人工鱼群群聚行为和追尾行为最优方向的前进速度。并在此基础上与遗传算法、粒子群算法和量子免疫算法作了对比分析。结果表明:追尾行为最优方向的前进速度优于群聚行为,二进制人工鱼群算法综合性能优于遗传算法、粒子群算法和量子免疫算法。研究表明二进制人工鱼群算法具有收敛速度快、种群规模小和搜索能力强的特点。     

Replicating web contents using a hybrid particle swarm optimization

We consider the problem of placing copies of objects in a distributed web server system to minimize the cost of serving read and write requests when the web servers have limited storage capacities. We formulate the problem as a 0–1 optimization problem and present a hybrid particle swarm optimization algorithm to solve it. The proposed hybrid algorithm makes use of the strong global search ability of particle swarm optimization (PSO) and the strong local search ability of tabu search to obtain high quality solutions. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithm (GA), simple PSO, tabu search, and random placement algorithm on a variety of test cases. The simulation results indicate that the proposed hybrid approach outperforms the GA, simple PSO, and tabu search.     

基于多目标优化的信用风险管理

信用风险管理是银行管理的核心内容。近年虽然不乏测度银行信用风险方面的研究,但都只限于考虑风险最低的单目标模型。以追求风险与收益系统均衡为出发点,提出了测度银行信用风险的二元目标优化模型,解决了信用风险管理过程中低风险和高收益的辨证统一问题。将遗传算法引进二元目标优化模型的求解,极大地提高了模型的求解效率。     

基于SVM和遗传算法的建模与全局寻优方法

系统建模与寻找函数的全局最优解是很常见的工业应用问题。本文首先讨论使用支持向量机来根据由系统中提取的样本数据进行函数拟合,然后将所得到的函数作为目标函数,介绍了用遗传算法寻找函数最优解的步骤,并对优化结果进行了检验,结果表明了遗传算法具有良好的全局快速搜索能力。     

Multi-objective meta level soft computing-based evolutionary structural design

Evolutionary structural design has been the topic of much recent research; however, such designs are usually hampered by the time-consuming stage of prototype evaluations using standard finite element analysis (FEA). Replacing the time-consuming FEA by neural network approximations may be a computationally efficient alternative, but the error in such approximation may misguide the optimization procedure. In this paper, a multi-objective meta-level (MOML) soft computing-based evolutionary scheme is proposed that aims to strike a balance between accuracy vs. computational efficiency and exploration vs. exploitation. The neural network (NN) is used here as a pre-filter when fitness is estimated to be of lesser significance while the standard FEA is used for solutions that may be optimal in their current population. Furthermore, a fuzzy controller updates parameters of the genetic algorithm (GA) in order to balance exploitation vs. exploration in the search process, and the multi-objective GA optimizes parameters of the membership functions in the fuzzy controller. The algorithm is first optimized on two benchmark problems, i.e. a 2-D Truss frame and an airplane wing. General applicability of the resulting optimization algorithm is then tested on two other benchmark problems, i.e. a 3-layer composite beam and a piezoelectric bimorph beam. Performance of the proposed algorithm is compared with several other competing algorithms, i.e. a fuzzy-GA-NN, a GA-NN, as well as a simple GA that only uses only FEA, in terms of both computational efficiency and accuracy. Statistical analysis indicates the superiority as well as robustness of the above approach as compared with the other optimization algorithms. Specifically, the proposed approach finds better structural designs more consistently while being computationally more efficient.