共查询到18条相似文献,搜索用时 78 毫秒
1.
2.
一种改进选择算子的遗传算法 总被引:2,自引:1,他引:1
遗传算法(Genetic Algorithm,GA)是一种模拟生物进化的智能算法,被广泛应用于求解各类问题。简单遗传算法(Simple GA)仅靠变异产生新的数值,常常存在搜索精确度不高的问题。针对这个问题,对SGA的选择算子进行改进,即把相似个体分在同一组中,以组为单位进行选择,并通过该组个体的特点进行高斯搜索生成新的群体。这样使得GA在搜索过程中不仅可以很好地保持个体的多样性,并且可以提高解的精确度。通过对11个函数(单峰和多峰)的仿真实验,证明了采用新的选择算子后,GA在求解问题的精确度上有了很大地改善。 相似文献
3.
针对传统遗传算法(SGA)容易"早熟"的不足,提出一种求解0-1背包问题(KP)的改进遗传算法。借鉴二重结构编码的解码处理方法设计了一种新解码方法,在保证解可行性的同时修正种群中无对应可行解的个体;采用模拟退火算法和改进的精英选择算子改进SGA。实例仿真结果验证了改进遗传算法在进化效率和最优解搜索能力上的优越性。 相似文献
4.
合理的资源调度可以在很大程度上提高网格系统资源的利用率,遗传算法(GA)因具有强大稳健的隐并行解空间搜索功能,被广泛应用于任务分配和调度问题的求解。本文在研究标准遗传算法(SGA)的基础上,提出与小生境技术相结合的自适应选择概率、父子竞争(PCC)交叉算子、插入变异算子和最优保存策略,改进SGA算法,在很好地保持种群收敛性的同时,提高了算法的局部和全局搜索能力。仿真实验结果表明,本文算法与其它调度算法比较,更能有效地实现资源的分配,可以成功应用于网格环境下独立任务的分配与调度。 相似文献
5.
唐德翠 《计算技术与自动化》2009,28(4):17-19
遗传算法(GA)是一种基于群智能的全局随机优化算法。针对简单遗传算法(SGA)收敛速度慢、易于早熟等缺点,采用改进的自适应交叉算子和自适应变异算子。结合兼顾性能指标和响应过程平衡的适配函数,以多种改进方式相结合的遗传算法对PID参数进行寻优整定。并将该控制器应用于纸浆漂白温度控制中,仿真结果表明:改进遗传算法能够明显改善收敛速度和寻优效果,当被控对象存在较大纯滞后、时间常数特性较大时,采用本方法优化PID控制器参数可获得比较满意的控制效果。 相似文献
6.
相比传统的调节方法,遗传算法具有更好的鲁棒性、最优性,能较好的实现参数的自动化调节。对标准遗传算法(SGA)进行了分析、研究,并在SGA的基础上进行了改进。改进的遗传算法从提高全局搜索性能和加快收敛速度出发,提出了改进的选择算子、交叉算子和变异算子,仿真结果表明,改进的遗传算法的全局搜索性能和收敛速度远远优于标准遗传算法。 相似文献
7.
嵌套式模糊自适应遗传算法 总被引:2,自引:0,他引:2
针对简单遗传算法(SGA)收敛速度慢和早熟收敛现象,将模糊逻辑理论应用于遗传算法,并采用两级嵌套的遗传算法,随主遗传算法GA1求解优化问题的进化进程用模糊控制的方法自适应地调整遗传算法的交叉概率和变异概率;利用另一个遗传算法GA2优化模糊规则库,实现了一种嵌套式模糊自适应遗传算法(NFAGA)。仿真结果表明,这种算法的全局搜索收敛速度和解的质量明显优于SGA和一般的自适应遗传算法(AGA)。 相似文献
8.
双变异算子遗传算法的应用 总被引:1,自引:1,他引:0
针对简单遗传算法(SGA)所存在的缺点和不足,提出了一种新的改进遗传算法一双变异算子GA.该算想法通过将所有产生的子代个体与父代个体混合作为下一代种群,在种群选择前对适应度值较低的个体进行一次变异,然后通过选择、交叉,再一次变异产生新种群,再利用自适应算法改变交叉和变异率及最优保存策略保护历代最优个体,利用matlab软件编程计算,在TSP中得到了较好的优化结果.实例说明,双变异算子的遗传算法能够最大限度使种群多样性,这样最有可能得到最优解,也易突破局部收敛的局限而达到全局最优. 相似文献
9.
10.
11.
In this study, a new mutation operator is proposed for the genetic algorithm (GA) and applied to the path planning problem of mobile robots in dynamic environments. Path planning for a mobile robot finds a feasible path from a starting node to a target node in an environment with obstacles. GA has been widely used to generate an optimal path by taking advantage of its strong optimization ability. While conventional random mutation operator in simple GA or some other improved mutation operators can cause infeasible paths, the proposed mutation operator does not and avoids premature convergence. In order to demonstrate the success of the proposed method, it is applied to two different dynamic environments and compared with previous improved GA studies in the literature. A GA with the proposed mutation operator finds the optimal path far too many times and converges more rapidly than the other methods do. 相似文献
12.
一种改进选择算子和基于小生境的遗传算法 总被引:5,自引:0,他引:5
陈友文 《计算机与数字工程》2009,37(6):21-24
为了进一步提高遗传算法选择算子的选优能力,在传统轮盘赌的基础上提出了一种基于排序的多轮轮盘赌选择算子,在提高了算子选优能力的同时也减少了随机性所产生的误差;同时采用了小生境技术,使得算法既能保证多样性又能够保留最优解。实验表明,与简单遗传算法相比较,新算法能够有效地提高收敛速度。 相似文献
13.
This paper highlights the potential of using genetic algorithms to solve cellular resource allocation problems. The objective in this work is to gauge how well a GA-based channel borrower performs when compared to a greedy borrowing heuristic. This is needed to establish how suited GA-like (stochastic search) algorithms are for the solution of optimization problems in mobile computing environments. This involves the creation of a simple mobile networking resource environment and design of a GA-based channel borrower that works within this environment. A simulation environment is also built to compare the performance of the GA-based channel-borrowing method with the heuristic. To enhance the performance of the GA, extra attention is paid to developing an improved mutation operator. The performance of the new operator is evaluated against the heuristic borrowing scheme. For a real-time implementation, the GA needs to have the properties of a micro GA strategy. This involves making improvements to the crossover operator and evaluation procedure so the GA can converge to a "good" solution rapidly. 相似文献
14.
Kazarlis S.A. Papadakis S.E. Theocharis J.B. Petridis V. 《Evolutionary Computation, IEEE Transactions on》2001,5(3):204-217
We investigate the potential of a microgenetic algorithm (MGA) as a generalized hill-climbing operator. Combining a standard GA with the suggested MGA operator leads to a hybrid genetic scheme GA-MGA, with enhanced searching qualities. The main GA performs global search while the MGA explores a neighborhood of the current solution provided by the main GA, looking for better solutions. The MGA operator performs genetic local search. The major advantage of MGA is its ability to identify and follow narrow ridges of arbitrary direction leading to the global optimum. The proposed GA-MGA scheme is tested against 13 different schemes, including a simple GA and GAs with different hill-climbing operators. Experiments are conducted on a test set including eight constrained optimization problems with continuous variables. Extensive simulation results demonstrate the efficiency of the proposed GA-MGA scheme. For the same number of fitness evaluations, GA-MGA exhibited a significantly better performance in terms of solution accuracy, feasibility percentage of the attained solutions, and robustness 相似文献
15.
In this study, a new mutation operator has been developed to increase Genetic Algorithm (GA) performance to find the shortest distance in the known Traveling Salesman Problem (TSP). We called this method as Greedy Sub Tour Mutation (GSTM). There exist two different greedy search methods and a component that provides a distortion in this new operator. The developed GSTM operator was tested with simple GA mutation operators in 14 different TSP examples selected from TSPLIB. The application of this GSTM operator gives much more effective results regarding to the best and average error values. The GSTM operator used with simple GAs decreases the best error values according to the other mutation operators with the ratio of between 74.24% and 88.32% and average error values between 59.42% and 79.51%. 相似文献
16.
单亲遗传算法及其全局收敛性分析 总被引:77,自引:0,他引:77
序号编码的遗传算法(GA)不能在两条染色体的任意位置进行交叉,必须使用
PMX,CX和OX等特殊的交叉算子,而这些交叉算子实施起来都很麻烦.针对序号编码GA
的上述不足,提出一种单亲遗传算法(PGA).PGA采用序号编码,不使用交叉算子,而代之以
隐含序号编码GA交叉算子功能的基因换位等遗传算子,简化了遗传操作,并且不要求初始
群体具有多样性,也不存在"早熟收敛"问题.仿真结果验证了这种算法的有效性. 相似文献
17.
18.
遗传算法收敛性的动力学分析及其应用 总被引:5,自引:1,他引:5
遗传算法的收敛性,特别是交叉算子的作用,一直缺乏深入的理论分析,当系统动力学的方法被应用于遗传算法的运行机理分析时,可以探讨在没有变异算子情况下遗传算法的收敛性问题,从而,明确了局部极值点的含义,指出了局部极值点的存在性和存在条件,证明了遗传算法在局部极值点附近的收敛性,并针对遗传算法的各种改进给出了理论上的依据,提出了遗传算法改进的方向。 相似文献