Search - TSP problem parallel

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[AI-NN-PR] parallelgenetic DL : 0: 使用并行遗传算法解决TSP问题，使用MPI函数库进行通信。-Using parallel genetic algorithm to solve TSP problem, use the MPI library for communication.
Date : 2025-12-28 Size : 1.25mb User : wrq
[AI-NN-PR] Parallel_Artificial_Immune_Algorithm_for_Large_Sca DL : 0: 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
Date : 2025-12-28 Size : 425kb User : 崔冰
[AI-NN-PR] ant DL : 0: 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.
Date : 2025-12-28 Size : 2kb User : 咋都有