共查询到20条相似文献,搜索用时 31 毫秒
1.
This paper implores the possible intervention of computers in the generative (concept) stage of settlement planning. The objective was to capture the complexity and character of naturally grown fishing settlements through simple rules and incorporate them in the process of design. A design tool was developed for this purpose. This design tool used a generative evolutionary design technique, which is based on multidisciplinary methods. Facets of designing addressed in this research are:
- •allocation of each design element's space and geometry,
- •defining the rules, constraints and relationships governing the elements of design,
- •the purposeful search for better alternative solutions,
- •quantitative evaluation of the solution based on spatial, comfort, complexity criterions to ensure the needed complexity, usability in the solutions.
2.
当前在解决资源优化配置问题时往往使用贪婪算法、遗传算法等.但贪婪算法只能选择一个最优度量标准,所以只能获得度量意义下的最优解而不是该问题的最优解,而如果直接使用遗传算法又存在搜索空间过大、耗时过长的问题.提出了一种新的算法.先基于贪婪算法获得问题的初始解空间,然后对初始解空间进行冲突检测与消解,最后运用改进的遗传算法进行优化获得最优方案.测试算例表明大大缩小了遗传算法的搜索空间,在保证获得最优解的条件下加快了收敛速度并有效防止了种群的退化.提出的算法在突发事务的处理方面具有一定的意义. 相似文献
3.
遗传算法求解VRP问题 总被引:9,自引:0,他引:9
李向阳 《计算机工程与设计》2004,25(2):271-273,276
在分析了许多求解固定车辆路径问题的优化算法后,提出了一种新的求解固定车辆路径问题的遗传算法。该算法的核心在于构建一种新的染色体编码,并且将“Inver-0ver”遗传操作算子与禁忌搜索算法结合起来,利用种群的信息引导种群的进化。引入动态非法检测来淘汰不合法个体,扩展了解空间并加快了搜索速度。经过大量的实例测试,该遗传算法增强了群体演化的质量,提高了算法收敛速度,能够找到比较好的近似最优解。 相似文献
4.
Qingfu Zhang Jianyong Sun Tsang E. 《Evolutionary Computation, IEEE Transactions on》2005,9(2):192-200
Estimation of distribution algorithms sample new solutions (offspring) from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The location information of solutions found so far (i.e., the actual positions of these solutions in the search space) is not directly used for generating offspring in most existing estimation of distribution algorithms. This paper introduces a new operator, called guided mutation. Guided mutation generates offspring through combination of global statistical information and the location information of solutions found so far. An evolutionary algorithm with guided mutation (EA/G) for the maximum clique problem is proposed in this paper. Besides guided mutation, EA/G adopts a strategy for searching different search areas in different search phases. Marchiori's heuristic is applied to each new solution to produce a maximal clique in EA/G. Experimental results show that EA/G outperforms the heuristic genetic algorithm of Marchiori (the best evolutionary algorithm reported so far) and a MIMIC algorithm on DIMACS benchmark graphs. 相似文献
5.
多星测控调度是一个具有大搜索空间的多峰问题。针对简单遗传算法求解易陷入局部最优和不稳定的缺陷,借鉴分散搜索多样化采样、局部寻优的特点,提出一种基于分散搜索的混合遗传算法,在全局的随机搜索中嵌入全局的定向搜索。在描述问题的基础上,提出可进行细粒度搜索的可行解表示方式,构建算法的整体流程,并设计由输入参数控制的多样化初始集产生方法、基于质量和多样性原则的参考集生成和更新方法、吸取被组合个体优良成份的解组合方法及基于启发式局部搜索的解提高方法等算法要素。仿真表明新算法在求解质量上比简单遗传算法有明显提高。 相似文献
6.
《Computers & Geosciences》2006,32(2):230-239
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or “good” initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. 相似文献
7.
Optimizing railway alignments is a quite complex and time-consuming engineering problem. The huge continuous search space, complex constraints, implicit objective function and infinite potential alternatives of this problem pose many challenges. Especially in mountainous regions, finding a near-optimal alignment for extremely complex terrain and constraints is a most arduous task, which cannot be solved satisfactorily with most existing methods. In this study, a stepwise & hybrid particle swarm-genetic algorithm is developed for railway alignment optimization in mountainous regions. It is a continuous search method suitable for railway alignment design. A stepwise horizontal–vertical–integral approach which defines the horizontal and vertical alignments as two kinds of particles, is proposed to solve the three-dimensional railway alignment optimization problem. To enhance the initial diversity and momentum, butterfly-shaped areas are preset on a path generated with a bidirectional distance transform for initializing horizontal particles. For the solution method, specific genetic operators, including roulette wheel selection, four crossovers and two mutations are integrated into the stepwise particle swarm method to address parameter-dependent performance and avoid premature convergence. In addition, a cubic polynomial weight update strategy is employed for thoroughly searching the problem space. This synthesis method has been applied to a real-world case in a very mountainous region. The detailed data analyses demonstrate that it can offer more promising solutions compared with alternatives designed by experienced designers and those generated with a genetic algorithm or non-stepwise particle swarm algorithm. 相似文献
8.
9.
Geometric problems defined by constraints have an exponential number of solution instances in the number of geometric elements involved. Generally, the user is only interested in one instance such that besides fulfilling the geometric constraints, exhibits some additional properties. Selecting a solution instance amounts to selecting a given root every time the geometric constraint solver needs to compute the zeros of a multi valuated function. The problem of selecting a given root is known as the Root Identification Problem.In this paper we present a new technique to solve the root identification problem. The technique is based on an automatic search in the space of solutions performed by a genetic algorithm. The user specifies the solution of interest by defining a set of additional constraints on the geometric elements which drive the search of the genetic algorithm. The method is extended with a sequential niche technique to compute multiple solutions. A number of case studies illustrate the performance of the method. 相似文献
10.
复杂参数产品形态设计中的解空间降维方法 总被引:4,自引:0,他引:4
针对产品形态设计中评价标准的模糊性问题,探索了智能化设计方法中的操作性技术.基于产品的形态编码,采用参数权重集中曲线的噪声度自动评测方式对解空间进行降维处理,并对产品形态编码分级.在降维后的系列解空间中使用交互式遗传算法搜索最优方案,并逐级完成形态的细化设计.文中方法从用户交互选择信息中提取出各参数对产品形态的重要性等级,作为细化设计过程的依据.求解过程全部基于解码的形态方案进行,为设计师的工作提供了直观的界面. 相似文献
11.
12.
基于遗传算法模式匹配的机器人实时视觉伺服 总被引:3,自引:0,他引:3
对于机器人手臂来讲 ,对工作环境的识别是完成一个智能任务的最重要的问题之一 .因为这种智能可以使它工作在一个变化的环境中 .本文提出了一种新的机器人手臂的控制策略 ,可以利用视觉信息来指导机器人的手臂在它的工作空间中捡起一个已知形状但任意位置和方向的物体 .在对物体的搜索过程中 ,利用基于视觉闭环的视觉伺服来完成对机器人手臂的运动控制 .本系统利用遗传算法 (Genetic Algorithm ,GA)和模式匹配技术完成对搜索空间的搜索并获得了良好的结果 .本文完成了对带有两连杆手臂的视觉伺服系统的仿真 ,仿真结果证明了算法的有效性 相似文献
13.
This work studies a nonlinear optimization problem subject to fuzzy relational equations with max-t-norm composition. Since the feasible domain of fuzzy relational equations with more than one minimal solution is non-convex, traditional nonlinear programming methods usually cannot solve them efficiently. This work proposes a genetic algorithm to solve this problem. This algorithm first locates the feasible domain through the maximum solution and the minimal solutions of the fuzzy relational equations, to significantly reduce the search space. The algorithm then executes all genetic operations inside this feasible domain, and thus avoids the need to check the feasibility of each solution generated. Moreover, it uses a local search operation to fine-tune each mutated solution. Experimental results indicate that the proposed algorithm can accelerate the searching speed and find the optimal solution. 相似文献
14.
Mehrdad Hakimi-Asiabar Seyyed Hassan Ghodsypour Reza Kerachian 《Computers & Industrial Engineering》2009,56(4):1566-1576
Genetic Algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAs is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations.In this paper, a new method called SOM-Based Multi-Objective GA (SBMOGA) is proposed to improve the genetic diversity. In SBMOGA, a grid of neurons use the concept of learning rule of Self-Organizing Map (SOM) supporting by Variable Neighborhood Search (VNS) learn from genetic algorithm improving both local and global search. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm is developed to enhance the local search efficiency in the Evolutionary Algorithms (EAs). The SOM uses a multi-objective learning rule based-on Pareto dominance to train its neurons. The neurons gradually move toward better fitness areas in some trajectories in feasible space. The knowledge of optimum front in past generations is saved in form of trajectories. The final state of the neurons determines a set of new solutions that can be regarded as the probability density distribution function of the high fitness areas in the multi-objective space. The new set of solutions potentially can improve the GAs overall efficiency. In the last section of this paper, the applicability of the proposed algorithm is examined in developing optimal policies for a real world multi-objective multi-reservoir system which is a non-linear, non-convex, multi-objective optimization problem. 相似文献
15.
16.
Modified fuzzy ants clustering approach 总被引:1,自引:1,他引:0
Being trapped in local optima within clustering search space currently is nontrivial difficulty. In order to relieve such
a difficulty, even using genetic algorithm to optimize the initial clusters for fuzzy c-means is still unsatisfied. Since
genetic algorithm intensifies only the current best solution, it will easily gets trapped in local minima. The ant colony
system, dissimilarly to genetic algorithm, recognizes that the solutions near the best solution are also good ones and they
bring about smoothness of solution. This paper proposes a modified fuzzy ant clustering. Such a presented method is a combination
of genetic algorithm, ant colony system and fuzzy c-means. It is employed in creating fuzzy color histogram in image retrieval
application. The performance measurement relates to the percentages of accuracy of image retrieval. Experimental results show
that the proposed approach yields the best results among others with respect to sensitivity and robustness on dealing with
lighting intensity changes, quantization errors, also changes in number of images and in size of color space, even the certain-range
variation of a particular parameter of clustering. 相似文献
17.
利用EST(Expresscd Sequence Tag)序列数据发现新基因,是当前国际上基因组研究的热点,但程序设计十分复杂。计算量非常巨大。而遗传算法是一种能在复杂而庞大的搜索空间中利用问题的固有知识来缩小搜索范围,避免组合爆炸,从而得到最优解或准最优解的通用搜索算法。该文结合核酸序列的特征,提出了一种改进的并行遗传算法,应用于EST序列拼接的组合优化。 相似文献
18.
Lingyun Wei Tianbing Tang Xianghong Xie Wenjie Shen 《Structural and Multidisciplinary Optimization》2011,43(5):665-682
Truss shape and sizing optimization under frequency constraints is extremely useful when improving the dynamic performance
of structures. However, coupling of two different types of design variables, nodal coordinates and cross-sectional areas,
often lead to slow convergence or even divergence. Because shape and sizing variables coupled increase the number of design
variables and the changes of shape and sizing variables are of widely different orders of magnitude. Otherwise, multiple frequency
constraints often cause difficult dynamic sensitivity analysis. Thus optimal criteria and mathematical programming methods
have considerable limitations on solving the problems because of needing complex dynamic sensitivity analysis and being easily
trapped into the local optima. Genetic Algorithms (GAs) show great potentials to solve the truss shape and sizing optimization
problems. Since GAs adopt global probabilistic population search techniques and require no gradient information. The improved
genetic algorithms can effectively increase the solution quality. However, the serial GA is computationally expensive and
is limited on gaining higher quality solutions. To solve the truss shape and sizing optimization problems with frequency constraints
more effectively and efficiently, a Niche Hybrid Parallel Genetic Algorithm (NHPGA) is proposed to significantly reduce the
computational cost and to further improve solution quality. The NHPGA is to blend the advantages of parallel computing, simplex
search and genetic algorithm with niche technique. Several typical truss optimization examples demonstrate that NHPGA can
significantly reduce computing time and attain higher quality solutions. It also suggests that the NHPGA provide a potential
algorithm architecture, which effectively combines the robust and global search characteristics of genetic algorithm, strong
exploitation ability of simplex search and computational speedup property of parallel computing. 相似文献
19.
QoS multicast routing is a non-linear combinatorial optimization problem. It tries to find a multicast routing tree with minimal cost that can satisfy constraints such as bandwidth, delay, and delay jitter. This problem is NP-complete. The solution to such problems is often to search first for paths from the source node to each destination node and then integrate these paths into a multicast tree. Such a method, however, is slow and complex. To overcome these shortcomings, we propose a new method for tree-based optimization. Our algorithm optimizes the multicast tree directly, unlike the conventional solutions to finding paths and integrating them to generate a multicast tree. Our algorithm also applies particle swarm optimization to the solution to control the optimization orientation of the tree shape. Simulation results show that our algorithm performs well in searching, converging speed and adaptability scale. 相似文献