基于改进初始解的遗传算法的布局设计方法

带性能约束的三维布局问题属于具有很强应用背景的组合优化问题,进行了基于全局的布局求解方法的探索。由于NP完全问题的计算复杂性,使得遗传算法求解问题的全局最优解时效率较低。改进了遗传算法的初始解,对提高算法的效率进行了研究。并以旋转卫星舱布局的简化模型为背景,建立了多目标优化数学模型。实例结果与传统遗传算法以及乘子法的计算结果比较,表明该算法具有较好的求解效率。     

基于学习的遗传算法及其在布局中的应用

布局问题属于具有很强应用背景的组合优化问题,除其内在的NP完全的计算复杂性,布局还包括约束复杂性问题和布局物体与空间的形状复杂性问题。针对布局求解中存在的问题,该文进行了基于全局优化的布局求解方法研究。布局问题中有一类关于复杂分片光滑连续函数全局优化算法,但目前的各种遗传算法的效率和精度不能令人满意。文中从生物可以从环境中学习生存技巧、自主的趋利避害的思路出发,增加了学习算子,引用函数的局部信息,构造拟牛顿方向,令每个个体在当前状态下有目的地搜索,最有效的向局部最优点趋进。通过典型测试函数与传统遗传算法,模拟退火算法,复合形法进行比较验算,表明该算法具有优良的求解质量和较好的求解效率;并以旋转卫星舱布局的简化模型为背景,建立多目标优化数学模型,与传统遗传算法和乘子法的计算结果比较,该算法求解的质量和效率更优。该文研究表明,基于学习的遗传算法在布局优化中具有应用潜力;启发式随机搜索策略和局部优化算法相结合的求解方案是解决复杂函数优化的有效途径。     

基于改进蚁群算法的纳什均衡求解

在基本蚁群算法寻优机制的基础上,提出一种用于求解有限n人非合作博弈的纳什均衡解的改进蚁群算法。在全局搜索中,引入遗传算法中的交叉和变异操作提高算法的全局搜索能力。在局部搜索中,嵌入动态随机搜索技术使算法加速收敛到最优解,并通过引入控制步长调整随机搜索向量,保证蚁群始终在混合策略空间内。算例测试结果表明,与传统的遗传算法相比,该算法具有更好的计算性能。     

基于遗传算法的物流配送路径优化问题研究

遗传算法是一种基于自然进化原理的全局搜索随机算法。遗传算法在选址问题、配送问题、调度问题、运输问题、布局问题方面意义重大。在建立物流配送路径优化问题数学模型的基础上,构造了求解该问题的遗传算法。该遗传算法采用常用的二进制编码,在个体选择上结合使用最优个体保留策略和轮盘赌法。最后以这种方法进行了实验计算,通过计算结果表明,用遗传算法进行物流配送路径优化,可以方便有效地求得问题的最优解或近似最优解。     

基于遗传算法的物流配送路径优化问题研究

遗传算法是一种基于自然进化原理的全局搜索随机算法.遗传算法在选址问题、配送问题、调度问题、运输问题、布局问题方面意义重大.在建立物流配送路径优化问题数学模型的基础上,构造了求解该问题的遗传算法.该遗传算法采用常用的二进制编码,在个体选择上结合使用最优个体保留策略和轮盘赌法.最后以这种方法进行了实验计算,通过计算结果表明,用遗传算法进行物流配送路径优化,可以方便有效地求得问题的最优解或近似最优解.     

求解不等圆布局问题的一类遗传算法物方法

在已有求解不等圆布局问题算法的基础上 ,根据问题特点提出了一类遗传算法 ,通过将拟物方法与标准遗传算法结合使用 ,较好地解决了对布局优化函数进行全局最优求解的问题 最后通过实例计算验证了本算法的有效性 .     

基于融合算法的测试优化选择问题研究

测试优化选择是个集覆盖问题,而启发式算法是求解集覆盖问题的有效方法。文中将遗传算法、BP神经网络和模拟退火算法进行融合,提出了一种融合算法,该算法充分利用遗传算法全局搜索能力强、BP神经网络训练能力强和模拟退火算法搜索速度快的优点,既避免陷入局部最优的现象,又提高了搜索的效率和精度。该算法已应用于求解测试优化问题。实例证明,该算法能够快速有效地求得测试优化问题的最优解。     

一种融合模拟退火和单亲遗传的优化求解算法

度约束最小生成树是一个经典的组合优化NP难题,其在网络设计和优化中有广泛的应用;现有求解方法往往不能很好地兼顾求解效率和求解精度;为了在缩短求解时间的同时,更好地获得最优解,提出了一种结合模拟退火算法和单亲遗传算法的改进求解算法;首先,改进遗传算法中变异因子的生成方式,避免不可行解个体的产生,并且设计自适应变异率,以提高算法的求解效率;其次,针对单亲遗传算法仅有变异操作可能导致最优解个体跳跃的问题,结合模拟退火的思想,来保证解的全局最优性;最后,在具体的度约束最小生成树问题中进行了三组实验,从运行时间和最优解的情况等方面与传统单亲遗传算法进行对比,实验表明该算法在求解效率和获得最优解方面都有较好的改进效果。     

蚁群遗传算法对于TSP问题的应用

遗传算法和蚁群算法是两种具有代表性的智能算法。在解决组合优化问题时,遗传算法具有较快的全局搜索能力,但在解决规模较大的TSP问题时存在一定缺陷,不能取得全局最优解。相反蚁群算搜索速度相对较慢,但有着较高的准确性,对于大规模问题有较好的效果。本文改进了两种算法,将蚁群算法与遗传算法融化起来。首先借助遗传算法的快速搜索能力,快速接近最优解,通过求解结果为蚁群算法设置初始信息量,再借助蚁群算法进行最终结果的求解,得到最优解。经过计算机仿真发现,在一定情况下,新的改进算法对TSP问题的求解能力有一定提高。     

遗传算法在排课系统中的应用

遗传算法是模拟自然选择和遗传的一种随机搜索算法。由于排课问题是一个有约束的、多目标的、难解的组合优化问题,采用具有智能型和并行性的遗传算法,来对排课问题进行求解,是所有求解该问题方法中比较明智的选择。采用了遗传算法作为搜索近似最优解的算法。目的是研究自然系统的自适应行为,并用于设计具有自适应功能的软件系统。     

粒子群算法及其在布局优化中的应用

复杂工程布局(如卫星舱布局)方案设计问题,在理论上属带性能约束的布局优化问题(NPC问题),很难求解。论文以卫星舱布局为例,将粒子群算法(PSO)应用于布局问题,构造此类问题的粒子表达方法,建立了此类问题的粒子群算法。文中通过3个算例(其中一个为已知最优解的算例)的数值计算,验证了该算法的可行性和有效性。     

Explosion gravitation field algorithm with dust sampling for unconstrained optimization

Gravitation Field Algorithm (GFA) is a novel optimization algorithm derived from the Solar Nebular Disk Model (SNDM) in astronomy and inspired by the formation process of planets. Although it has achieved good performance when solving many unconstrained optimization problems, which demonstrated its promising application potential in many real-world problems, GFA still has much room for improvement, especially when it comes to the accuracy and efficiency of the algorithm.In this research, an improved GFA algorithm called Explosion Gravitation Field Algorithm (EGFA) is proposed for unconstrained optimization problems, with the introduction of two strategies: Dust Sampling (DS) and Explosion Operation. The task of DS is to locate the space that contains the optimal solution(s) by initializing the dust population randomly in the search space; while the Explosion Operator is to improve the accuracy of solutions and decrease the probability of the algorithm falling into local optima by generating the new population around the center dust to replace the original population.A comparison of experimental results on six classical unconstrained benchmark problems with different dimensions demonstrates that the proposed EGFA outperforms the original GFA and several classical metaheuristic optimization algorithms, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in terms of accuracy and efficiency in lower dimensions. Additionally, the comparison of results on three real datasets indicate that EGFA performs better than the original GFA and k-means for solving clustering problems.     

A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems

Whale Optimization Algorithm (WOA), as a new population-based optimization algorithm, performs well in solving optimization problems. However, when tackling high-dimensional global optimization problems, WOA tends to fall into local optimal solutions and has slow convergence rate and low solution accuracy. To address these problems, a whale optimization algorithm based on quadratic interpolation (QIWOA) is presented. On the one hand, a modified exploration process by introducing a new parameter is proposed to efficiently search the regions and deal with the premature convergence problem. On the other hand, quadratic interpolation around the best search agent helps QIWOA to improve the exploitation ability and the solution accuracy. Moreover, the algorithm tries to make a balance between exploitation and exploration. QIWOA is compared with several state-of-the-art algorithms on 30 high-dimensional benchmark functions with dimensions ranging from 100 to 2000. The experimental results show that QIWOA has faster convergence rate and higher solution accuracy than both WOA and other population-based algorithms. For functions with a flat or sharp bottom, QIWOA is difficult to find the global optimum, but it still performs best compared with other algorithms.     

Cellular genetic algorithm technique for the multicriterion design optimization

There have been increased activities in the study of genetic algorithms (GA) for problems of design optimization. The present paper describes a fine-grained model of parallel GA implementation that derives from a cellular-automata-like computation. The central idea behind the Cellular Genetic Algorithm approach is to treat the GA population as being distributed over a 2-D grid of cells, with each member of the population occupying a particular cell and defining the state of that cell. Evolution of the cell state is tantamount to updating the design information contained in a cell site, and as in cellular automata computations, takes place on the basis of local interaction with neighboring cells. A focus of the paper is in the adaptation of the cellular genetic algorithm approach in the solution of multicriteria design optimization problems. The proposed paper describes the implementation of this approach and examines its efficiency in the context of representative design optimization problems.     

云环境下方差定向变异遗传算法的任务调度

云环境下遗传算法（GA）的任务调度存在寻优能力差、结果不稳定等问题。对于上述问题,提出了一种基于方差与定向变异的遗传算法（V-DVGA）。在选择部分,在每一次迭代的过程中进行多次选择,利用数学方差来保证种群的多样性并扩大较优解的搜索范围。在交叉部分,建立新的交叉机制,丰富种群的多样性并提高种群整体的适应度。在变异部分,优化变异机制,在传统变异的基础上采用定向变异来提高算法的寻优能力。通过workflowSim平台进行云环境仿真实验,将此算法与经典的遗传算法和当前的基于遗传算法的工作流调度算法（CWTS-GA）进行比较。实验结果表明,在相同的设置条件下,该算法在执行效率、寻优能力和稳定性等方面优于其他两个算法,是一种云计算环境下有效的任务调度算法。     

基于QPSO的图像融合算法的研究*

提出了一种基于量子行为的粒子群优化算法(QPSO)的图像融合方法.将图像融合问题归结为最优化问题,采用了QPSO算法进行优化.QPSO不仅参数个数少,其每一个迭代步的取样空间能覆盖整个解空间,因此能保证算法的全局收敛.与PSO算法和遗传算法进行了比较,证明了QPSO算法在图像融合中具有良好的效果.     

求解旅行商问题的多尺度量子自由粒子优化算法

针对目前元启发式算法在求解组合优化问题中的旅行商问题(TSP)时求解缓慢的问题,受量子理论中波函数的启发提出一种多尺度自适应的量子自由粒子优化算法。首先,在可行域中随机初始化表示城市序列的粒子,作为初始的搜索中心;然后,以每个粒子为中心进行当前尺度下的均匀分布函数的采样,并交换采样位置上的城市编号产生新解;最后,根据新解相较上一次迭代中最优解的优劣进行搜索尺度的自适应调整,并在不同的尺度下进行迭代搜索直到满足算法结束条件。将该算法和混合粒子群优化(HPSO)算法、模拟退火(SA)算法、遗传算法(GA)和蚁群优化算法应用在TSP上进行性能测试,实验结果表明自由粒子模型算法适合求解组合优化问题,在TSP数据集上相比目前较优算法在求解速度上平均提升50%以上。     

An innovative framework for designing genetic algorithm structures

Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields. Like other meta-heuristic algorithms, Genetic Algorithms can only provide a probabilistic guarantee of the global optimal solution. Having a Genetic Algorithm (GA) capable of finding the global optimal solution with high success probability is always desirable. In this article, an innovative framework for designing an effective GA structure that can enhance the GA's success probability of finding the global optimal solution is proposed. The GA designed with the proposed framework has three innovations. First, the GA is capable of restarting its search process, based on adaptive condition, to jump out of local optima, if being trapped, to enhance the GA's exploration. Second, the GA has a local solution generation module which is integrated in the GA loop to enhance the GA's exploitation. Third, a systematic method based on Taguchi Experimental Design is proposed to tune the GA parameter set to balance the exploration and exploitation to enhance the GA capability of finding the global optimal solution. Effectiveness of the proposed framework is validated in 20 large-scale case study problems in which the GA designed by the proposed framework always outperforms five other algorithms available in the global optimization literature.     

On improving the GA step-wise shape optimization method through the application of the Fixed Grid FEA paradigm

In previous work by the authors, a Genetic Algorithm (GA) based shape optimization technique was introduced. The method was shown to be capable of producing high-fidelity optimal shapes. However, the process was computationally expensive and required constant re-meshing due to distorted boundary elements resulting from large boundary movements. This paper combines the Fixed Grid (FG) method of Finite Element Analysis (FEA) and the GA shape optimization module to create a hybrid that effectively addresses these problems. The FG solver is found to be significantly faster than conventional FEA, and the fixed FE mesh frees boundary movements from meshing constraints. The Fixed-Grid Genetic-Algorithm (FGGA) shape optimization method is detailed in this paper, and the key algorithms used in the FG and the GA components are explained. The method is also applied to a number of shape optimization problems, and the results are presented and discussed.