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Comparative study of prim and genetic algorithms in minimum spanning tree and travelling salesman problem

机译：最小生成树和旅行商问题中素数和遗传算法的比较研究

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Optimization is the essential thing in an algorithm. It can save the operational cost of an activity. At the Minimum Spanning Tree, the goal to be achieved is how all nodes are connected with the smallest weights. Several algorithms can calculate the use of weights in this graph. Genetic and Primary algorithms are two very popular algorithms for optimization. Prim calculates the weights based on the shortest distance from a graph. This algorithm eliminates the connected loop to minimize circuit. The nature of this algorithm is to trace all nodes to the smallest weights on a given graph. The genetic algorithm works by determining the random value as first initialization. This algorithm will perform selection, crossover, and mutation by the number of rounds specified. It is possible that this algorithm can not achieve the maximum value. The nature of the genetic algorithm is to work with probability. The results obtained are the most optimal results according to this algorithm. The results of this study indicate that the Prim is better than Genetics in determining the weights at the minimum spanning tree while Genetic algorithm is better for travelling salesman problem. Genetics will have maximum results when using large numbers of rotations and populations.
机译：优化是算法中的关键。它可以节省活动的运营成本。在最小生成树中，要实现的目标是如何以最小的权重连接所有节点。几种算法可以计算该图中权重的使用。遗传算法和主算法是两种非常流行的优化算法。 Prim根据与图形的最短距离来计算权重。该算法消除了连接的环路，以最大程度地减少电路。该算法的本质是在给定图上跟踪所有节点以最小的权重。遗传算法通过将随机值确定为首次初始化来工作。该算法将根据指定的轮数执行选择，交叉和变异。此算法有可能无法达到最大值。遗传算法的本质是具有概率。根据该算法，获得的结果是最佳结果。这项研究的结果表明，在确定最小生成树的权重方面，Prim优于遗传算法，而遗传算法更适合旅行商问题。当使用大量轮作和种群时，遗传学将获得最大的结果。

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