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In this paper three communication algorithms are proposed for two types of generalized hypercube multiprocessor. The algorithms are intended to solve three intensive communication problems: complete broadcast, single-node scatter and total exchange. The algorithms achieve both the time and transmission complexity bounds for the three problems on the balanced generalized hypercube (BGHC). The BGHC is a w^d-node network with w nodes along each of the d dimensions. These communication algorithms are performed based on a balanced spanning tree, called a compatible tree, which can be used to solve any of the tree problems. Several theoretical results related to the compatible tree and then the sufficient and necessary condition for concurrent transmissions are presented. The concurrent condition ensures the maximum use of network bandwidth so that the optimal bounds are achieved. It is shown that the proposed scheduling algorithms achieve the optimal bounds for any w and d.

We show that the problem of finding the element with the highest frequency in a set, the mode of the set, is bounded by the problem of sorting multisets. As a consequence, we obtained an improvement of the upper bound for finding the mode. This result was obtained by presenting an algorithm that requires linear time when the set is initially sorted. The same algorithm was parallelized to produce the first optimal parallel algorithm for finding the mode.

In this paper, we present an algorithm for a generalization of list ranking called computation list evaluation. As a consequence of the generalization and the existence of this algorithm for computation list evaluation, we obtain a generalization of Euler Tour technique. Finally, we present several applications of the generalized Euler Tour technique. Of interest in the applications is the identification of a set of problem instances that are solvable using tree contraction and which can alternatively be solved using a simple algorithm based on the generalized Euler Tour technique.

This paper deals with intelligent tuning by hybrid system composed of genetic algorithm and clonal selection (CS). This paper uses gain margin (GM) and phase margin (PM) to define the tuning margin of control system for optimal tuning. Up to this time, many intelligent algorithms have been tried in order to improve the intelligent controller performance for industrial areas such as power plant, motor control, chemical control, and so on. However, in the actual plant, they are many limitations in tuning approaching because of computing speed. Therefore, it is difficult to apply for getting optimum value in tuning. In this paper, novel computing scheme has been done by hybrid CS algorithm and genetic algorithm, GM, and PM to obtain an advanced value and effective approach for optimal parameters.