Description: 首先介绍了当前求解TSP问题最好的演化算法之一：GT算法。文中重点设计了在NOW上求解旅行商问题GT算法的并行算法，详细描述了设计思路，设计策略。还给出了相当详细的并行算法描述。文中设计的并行算法已经在NOW上的PVM平台上实现。关键词NOW，演化计算，旅行商问题,并行计算-first briefed on the current TSP evolutionary algorithm best one : GT algorithm. The article focused on the design of the NOW on solving the traveling salesman problem GT algorithm parallel algorithm, described in detail the design concept, design strategy. Also given a fairly detailed description of the parallel algorithm. The text of parallel algorithm design in NOW's PVM achieve. Keywords NOW, evolutionary computation, the traveling salesman problem, parallel computing Platform: |
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Author:巨金华 |
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Description: 在MPI平台下，用C语言实现模拟退火算法的货郎担问题的并行算法。-In the MPI platform, using C language to achieve simulated annealing algorithm traveling salesman problem of the parallel algorithm. Platform: |
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Author:俊峰 |
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Description: 一种求解TsP问题的新型人工神经网络方法
用自动波方法求解问题，该方法具有鲁棒
性和可靠性好、人规模并行计算等特点-Solving the problem TsP new artificial neural network method with an automatic-wave method to solve the problem, the method has the robustness and reliability, human-scale parallel computing and so on Platform: |
Size: 41984 |
Author:yonghuazhu |
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Description: 为求解大规模TSP 问题, 提出了并行人工免疫系统的塔式主从模型(TMSM), 和基于TMSM 的并行免疫记忆克隆选择算法(PIMCSA) TMSM
是粗粒度的两层并行人工免疫模型, 其设计体现了分布式的免疫响应和免疫记忆机制. PIMCSA 用疫苗的迁移代替了抗体的迁移, 兼顾了种群多样性的保持和算法的收敛速度. 与其他算法相比, PIMCSA 在求解精度和运行时间上都更具优势, 而且问题规模越大优势越明显. TMSM 很好地体现了免疫系统的特性, PIMCSA 是适合求解大规模复杂优化问题
的并行人工免疫算法, 具有良好的可扩展性.-This paper presents a parallel model termed as towerlike master slave model (TMSM) for artificial immune systems. Based on TMSM, the parallel immune memory clonal selection algorithm ( PIMCSA) is also designed for dealing with large scale TSP problems. TMSM is a two level coarse grained parallel artificial immune model with distributed immune response and dis
tributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than antibodies as has been done in parallel genetic algorithms, it is a good balance between the diversity maintenance of populations and the convergent speed of the algorithm. PIMCSA shows superiority over other compared approaches both in solution quality and computation time, and the
lager the problem size the more outstanding the predominance will be. TMSM is a good simulation of biological immune system, and PIMCSA is a parallel artificial immune algorithm with good extensibility, which is capable of solving large scale and c Platform: |
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Author:崔冰 |
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Description: 蚁群算法(ant colony algorithm，简称ACA)是20世纪90年代由意大利学者M．Dorigo等人首先提出来的一种新型的模拟进化算法．它的出现为解决NP一难问题提供了一条新的途径．用蚁群算法求解旅行商问题(TSP)、分配问题(QAP)、调度问题(JSP)等，取得了一系列较好的实验结果．虽然对蚁群算法研究的时间不长，但是初步研究已显示出蚁群算法在求解复杂优化问题(特别是离散优化问题)方面具有一定的优势，表明它是一种很有发展前景的方法．蚁群算法的主要特点是：正反馈、分布式计算．正反馈过程使它能较快地发现问题的较好解；分布式易于并行实现，将它与启发式算法相结合，易于发现较好解．-ACO (ant colony algorithm, referred to as ACA) is the 1990s by the Italian scholar M. Dorigo, who first proposed a new type of simulated evolutionary algorithm. It appears to solve NP-hard problem provides a new way. Ant colony algorithm for traveling salesman problem (TSP), distribution (QAP), scheduling problems (JSP), etc., made a series of good results. Although the ant colony algorithm is not long, but preliminary studies have shown that the ant colony algorithm in solving complex optimization problems (in particular, discrete optimization problem) has certain advantages, that it is a promising approach. The main features of ant colony algorithm is: positive feedback, distributed computing. Positive feedback process so that it can quickly find a better solution of the problem distributed easy-to-parallel implementation, it would be combined with the heuristic algorithm, easy to find better solutions. Platform: |
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Author:咋都有 |
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Description: 经典遗传算法利用单一种群对种群个体进行交叉、变异和选择操作，在进化过程中的超级个体易产生过早收敛现象，粗粒度并行遗传算法利用多个子种群进行进化计算，各子群体分别独立进行遗传操作，相互交换最优个体后继续进化。该文证明了该算法的搜索过程是一个有限时齐遍历马尔柯夫链，给出粗粒度并行遗传算法全局最优收敛性证明。对于旅行商问题TSP利用粗粒度并行遗传算法进行了求解，以解决经典遗传算法的收敛到局部最优值问题。仿真结果表明，算法的收敛性能优于经典遗传算法。-Classic genetic algorithm using a single population of individuals in a population cross, mutation and selection operation, the super individuals in the evolutionary process is easy to produce premature convergence phenomenon, coarse-grained parallel genetic algorithm using multiple sub-populations of evolutionary computation, various sub-groups, respectively, independent The genetic manipulation, the exchange of best individual continue to evolve. This paper shows that the search process of the algorithm is a finite homogeneous traverse the Markov chain, given the coarse-grained parallel genetic algorithm global optimal convergence proof. For the traveling salesman problem TSP coarse-grained parallel genetic algorithm to solve to solve the classic genetic algorithm converges to a local optimum value. The simulation results show that the convergence of the algorithm is superior to the classical genetic algorithm. Platform: |
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Author:陈嘉鑫 |
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Description: 蚁群算法解决TSP问题的并行化研究与实现-Research and Implementation of parallel ant colony algorithm to solve TSP problem Platform: |
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Author:吾辈 |
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Description: For this project I implemented a parallel formulation for the Traveling Salesman Problem (TSP). The TSP is a NP-hard problem in combinatorial optimization. Given a list of cities with their pairwise distance, the task is to find the shortest route that visits all the cities once and returns to the origin city. The problem was formulated in 1930 and is one of the most intensively studied problems in optimization with applications in planning, logistics, and the manufacturing of micro-chips. Platform: |
Size: 26624 |
Author:udin
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