Search algorithm

3.6. Algorithm of sequential gradient search Step 1: Set specifications of the inductor Step 2: Set the values of Bm ,  and no. of core steps. Step 3: 0.45≤ K ≤ 0.6 and 0.3 ≤ Rw ≤ 0.4 Step 4: i = 0 to 30 do: K  0.3  i / 100 Step 5: Calculate cost. Step 6: If cost shows initially low and after that high, (concavity fails for K ) go to step 28 Step 7: i = 0 to 20 do: Rw  2  i /10 Step 8: Go to sub-routine, calculate the cost. Step 9: If cost does not show low value and then high (concavity

From the tree SP we presented in the algorithm that we have obtained via Local Search Algorithm for STP, we have generated the matrix of cost. This is done by assigning a cost to all the edges of tree SP and by assigning a cost on “n” no. of nodes to all the other edges in graph. This assignment of cost helps in recognizing the cost of the longest possible path between a pair of nodes in any spanning tree is n−1 (i.e. it passes n−1 edges) while the cost of the shortest path between any pair of nodes

geographical positions of the nodes is usually a very complicated task, we propose a novel heuristic search technique to solve this problem. Our approach is a multi-population search algorithm based on the Particle Swarm Optimization (PSO). The goal of this algorithm is to search for sensor network layouts that maximize both the coverage and lifetime of the network. Unlike traditional PSO, our algorithm assignes a swarm to each sensor in the network and a global network topology is used to evaluate

between two destinations using popular search algorithms. As explained in the introduction, it is not feasible to attempt to run all the flights across the world through the search algorithms. Instead, flights from one airline, Delta, were selected. This includes code-share flights. The flight database that was used is from www.openflights.org and the data is current

rated a movie that belongs to the comedy genre, then the system can learn to recommend other movies from this genre. Collaborative filtering: The simplest and original implementation of this approach recommends to the active user the items that other users with similar tastes liked in the past. The similarity in taste of two users is calculated based on the similarity in the rating history of the users. This is the reason why collaborative filtering is often referred as “people-to-people correlation

A Survey of Hashing Techniques and its Applicability for Efficient Buffer Cache Management Abstract: Hashing is the convenient way to get access to an item based on the given key which is the requirement for efficient buffer cache management. Static hashing provides fastest access to an object at the cost of memory utilization, whereas sequential storage provides most efficient memory utilization at the cost of access time. To provide balance between two extremes dynamic hashing schemes are produced

recommendations. The drawbacks of collaborative and content-based methods is analyzed and proof of concept is provided for the need of deep learning based recommender systems. We introduce RBM algorithm and its applications for use in generic recommendation generation and propose to implement a deep learning neural networks algorithms(RBM) to create

incorporating users’ preferences regarding quality and diversities amongst web services. Users’ requirements are considered and mined from his usage history. Then we find functional similarities using clustering techniques followed by applying a ranking algorithm to list top-k services. To discover high quality Web services, a number of QoS models for Web services and QoS-driven service selection approaches have been proposed in the service computing field. In this system user explicitly specifies his/her

\section{Design Procedure} \label{Design} We divide the system into four main parts as follows. \begin {itemize} \item [1.] Modularize. \item [2.] Evaluation. \item [3.] Estimation. \item [4.] Testing \end{itemize} We represent this fact graphically in the following figure \ref{Figure:Phase}. Each part of the figure describes briefly. \begin{figure}[htp] \includegraphics[width=.48\textwidth]{figure/arc2.eps} \caption{Architecture of design procedure. } \label{Figure:Phase}

applied to find out the optimal generation of each unit when the generation cost curves are non-smooth and discontinuous in nature. Most of the PSO algorithms suffer from the problem of premature convergence in the early stages of the search and hence are unable to locate the global optimum. The idea here is to exercise proper control over the global and local exploration of the swarm during the optimization process. The PSO_TVAC based approach for practical non-convex ELD problem is tested on four