共查询到20条相似文献,搜索用时 62 毫秒
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针对木工板手工排样效率低和材料利用率低问题,提出木工板“一刀切”排样优化算法.在剩余矩形填充算法中添加启发式分块原则,改进的剩余矩形填充算法满足“一刀切”工艺要求.采用遗传算法对矩形件进行排样优化,以提高木工板利用率,降低企业生产成本.为提高算法的优化精度,使用基于指数变换的非线性动态适应度函数,引入精英保护策略,应用部分填充交叉(partially matched crossover)算子.结合剩余矩形填充“一刀切”算法对遗传种群进行解码计算原料利用率,并作为适应度函数值,进行迭代搜索最优解.排样实例表明木工板“一刀切”排样优化算法能够很好地解决多品种大规模木工板排样问题. 相似文献
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矩形优化排样问题是一个在制造业领域生产实践中普遍遇到的问题,采用了一种改进的最低水平线搜索算法求解此类问题.首先分析了原始的最低水平线搜索算法在排样中存在的缺陷,并针对该缺陷为其设计了一个评价函数,排样时对所有未排零件进行评价,选择评价值最高的零件排入当前位置,从而克服了算法在搜索过程中的随机性,优化了算法的搜索方向.实验仿真的结果表明,提出的算法可以得到较好的排样效果,并且其解决问题的规模越大,优化性能越好,适合于求解大规模排样问题. 相似文献
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对于"一刀切"矩形件优化排样问题,采用遗传算法与蚁群算法的混合算法进行研究.针对两种算法的传统混合策略和现有混合策略的不足,对两种算法的混合策略进行改进,并利用种群本身的染色体适值来判断种群进化是否停滞,确定了算法的最佳融合时机.对具体算例的分析验证表明,改进后的混合策略可有效减少算法的冗余迭代次数,提高搜索速度,是一种行之有效的排样算法. 相似文献
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针对理论上属于NPC问题的非规则件优化排样问题,论文提出一种基于小生境技术的自适应遗传模拟退火算法与基于内靠接临界多边形最低点的启发式布局算法相结合的方法。考虑到算法中交叉概率和变异概率的选择影响到算法收敛性,提出了自适应的交叉概率和变异概率,通过基于小生境技术的遗传模拟退火算法对非规则件排样的最优顺序和各自的旋转角度进行优化搜索。将非规则件定位在有缺陷原材料和非规则件多边形的内靠接临界多边形最低点以实现个体的解码,同时避开了原材料表面缺陷。排样实例表明,该优化排样算法行之有效,具有广泛的适应性。 相似文献
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Biswajit Mahanty Rajneesh Kumar Agrawal Shrikrishna Shrin Sourish Chakravarty 《Materials and Manufacturing Processes》2007,22(5):668-677
It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements—a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a novel one utilizing the Coulomb potential technique.
In the hybrid approach, the real-encoded genetic algorithm generates the order of the polygons while the coulomb potential algorithm determines the embodiment layout under the fixed combinations so as to minimize the scrap. The hybrid genetic algorithm is found to give better results for problems of larger size although it takes more computational time. 相似文献
In the hybrid approach, the real-encoded genetic algorithm generates the order of the polygons while the coulomb potential algorithm determines the embodiment layout under the fixed combinations so as to minimize the scrap. The hybrid genetic algorithm is found to give better results for problems of larger size although it takes more computational time. 相似文献
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提出了对Sutherland-Cohen算法的改进.通过引进辅助直线进行直线裁剪,只需两次求交运算就即求出线段的可见部分,还减少了对线段的重编码运算,具有和NLN算法相同高的效率.通过引入辅助平面进行三维裁剪,克服了NLN算法不能扩展到三维的缺点,而且其三维裁剪效率高于Sutherland-Cohen算法和梁友栋-Barsky算法. 相似文献
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Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science
and engineering fields. They are popular and have broad applications owing
to their high efficiency and low complexity. These algorithms are generally
based on the behaviors observed in nature, physical sciences, or humans. This
study proposes a novel metaheuristic algorithm called dark forest algorithm
(DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest
civilization, advanced civilization, normal civilization, and low civilization.
Each civilization has a unique way of iteration. To verify DFA’s capability,
the performance of DFA on 35 well-known benchmark functions is compared
with that of six other metaheuristic algorithms, including artificial bee colony
algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm,
grasshopper optimization algorithm, and whale optimization algorithm. The
results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when
solving high dimensional problems. DFA is applied to five engineering projects
to demonstrate its applicability. The results show that the performance of
DFA is competitive to that of current well-known metaheuristic algorithms.
Finally, potential upgrading routes for DFA are proposed as possible future
developments. 相似文献
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遗传算法求解大规模TSP时呈现出求解时间长、后期效率明显降低等缺陷。通过结合分块方法、局部搜索算法以及禁忌算法,本文提出一个求解TSP的混合算法,以提高初始解质量,减少计算量。利用遗传算法和混合算法对几个TSP进行数值实验,表明无论在结果的质量上还是在运行效率上,混合算法都明显优于遗传算法,而且,规模越大效果越明显。 相似文献
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Fatemeh Ahmadi Zeidabadi Mohammad Dehghani Pavel Trojovský Štěpán Hubálovský Victor Leiva Gaurav Dhiman 《计算机、材料和连续体(英文)》2022,72(1):399-416
Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA. 相似文献