Strategy Study on Maximum Degree and Bisection Degree Synchronization Search in Scale-Free Networks

2013 ◽  
Vol 303-306 ◽  
pp. 2157-2160
Author(s):  
Xin Yi Chen ◽  
Jian Hua Xia
Keyword(s):  
Maximum Degree ◽  
Search Strategy ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Search Efficiency ◽  
Network Congestion ◽  
Breadth First Search ◽  
Strategy Study ◽  
Scale Free ◽  
Search Speed

With the expansion of network and the increasing number of communities’ network, It’s a big problem for the search algorithm to enhance the search efficiency. The number of search steps and the amount of query information generated by maximum degree search strategy, which will grow exponentially, consequently, and lead to low the efficiency of search. Without considering the network congestion, breadth-first search strategy is undoubtedly the best search efficiency. From the point of the breadth-first search strategy, this paper designed and proposed the synchronous search strategy of Maximum degree and Bisection degree, and described the algorithm idea and algorithm design for MBDS. The simulation results showed that MBDS not only decreased the amount of query information, but also can efficiently decrease the search steps and improve the search speed.

Research on Vehicle Path Planning Base on Intelligent Transport System

2014 ◽  
Vol 513-517 ◽  
pp. 1822-1825
Author(s):  
Rui Wang ◽  
Na Wang
Keyword(s):  
Time Complexity ◽  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Intelligent Transport System ◽  
Dijkstra Algorithm ◽  
Intelligent Transport ◽  
Bidirectional Search ◽  
Search Speed ◽  
Vehicle Path

Typical shortest path is Dijkstra algorithm, its time complexity is O (n2). A map of the citys road network has many nodes, if we use the Dijkstra algorithm, the time complexity of the algorithm is too high and the speed of resolution this problem is slow. In order to change this situation, we are discussed from the aspects of algorithm design, puts forward the improved bidirectional search algorithm. Practice has proved that, the improved algorithm can improve the search speed and it suitable for intelligent transportation system.

scholarly journals Navigating in Trees with Permanently Noisy Advice

2021 ◽  
Vol 17 (2) ◽  
pp. 1-27
Author(s):  
Lucas Boczkowski ◽  
Uriel Feige ◽  
Amos Korman ◽  
Yoav Rodeh
Keyword(s):  
Phase Transition ◽  
Maximum Degree ◽  
Search Strategy ◽  
Partial Information ◽  
Search Algorithm ◽  
Search Problem ◽  
Expected Number ◽  
Noise Parameter ◽  
Faulty Node

We consider a search problem on trees in which an agent starts at the root of a tree and aims to locate an adversarially placed treasure, by moving along the edges, while relying on local, partial information. Specifically, each node in the tree holds a pointer to one of its neighbors, termed advice . A node is faulty with probability q . The advice at a non-faulty node points to the neighbor that is closer to the treasure, and the advice at a faulty node points to a uniformly random neighbor. Crucially, the advice is permanent , in the sense that querying the same node again would yield the same answer. Let Δ denote the maximum degree. For the expected number of moves (edge traversals) until finding the treasure, we show that a phase transition occurs when the noise parameter q is roughly 1 √Δ. Below the threshold, there exists an algorithm with expected number of moves O ( D √Δ), where D is the depth of the treasure, whereas above the threshold, every search algorithm has an expected number of moves, which is both exponential in D and polynomial in the number of nodes  n . In contrast, if we require to find the treasure with probability at least 1 − δ, then for every fixed ɛ > 0, if q < 1/Δ ɛ , then there exists a search strategy that with probability 1 − δ finds the treasure using (Δ −1 D ) O (1/ε) moves. Moreover, we show that (Δ −1 D ) Ω(1/ε) moves are necessary.

scholarly journals Improved Degree Search Algorithms in Unstructured P2P Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Guole Liu ◽  
Haipeng Peng ◽  
Lixiang Li ◽  
Yixian Yang ◽  
Qun Luo
Keyword(s):  
Success Rate ◽  
Search Algorithm ◽  
Memory Function ◽  
Small World ◽  
Search Algorithms ◽  
P2p Networks ◽  
Small World Networks ◽  
Breadth First Search ◽  
Network Resources ◽  
Scale Free

Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P) networks. Breadth-first search (BFS) and depth-first search (DFS) are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD) and memory function preference degree algorithm (PD). We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.

Study on Tabu Search Algorithm for Multi-Cargo Loading Problem

2012 ◽  
Vol 178-181 ◽  
pp. 1802-1805
Author(s):  
Chun Yu Ren
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Initial Solution ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Search Efficiency ◽  
Speed Of Convergence ◽  
Neighborhood Structure ◽  
Loading Problem

The paper is focused on the Multi-cargo Loading Problem (MCLP). Tabu search algorithm is an algorithm based on neighborhood search. According to the features of the problem, the essay centered the construct initial solution to construct neighborhood structure. For the operation, 1-move and 2-opt were applied, it can also fasten the speed of convergence, and boost the search efficiency. Finally, the good performance of this algorithm can be proved by experiment calculation and concrete examples.

Semi-Deterministic Construction of Scale-Free Networks with Designated Parameters

2018 ◽  
Vol 18 (01) ◽  
pp. 1850001
Author(s):  
NAOKI TAKEUCHI ◽  
SATOSHI FUJITA
Keyword(s):  
Degree Distribution ◽  
Interconnection Networks ◽  
Maximum Degree ◽  
Minimum Degree ◽  
Scale Free ◽  
Scale Free Networks ◽  
Ideal Number ◽  
Message Propagation ◽  
Deterministic Construction ◽  
The Ideal

Scale-free networks have several favorable properties as the topology of interconnection networks such as the short diameter and the quick message propagation. In this paper, we propose a method to construct scale-free networks in a semi-deterministic manner. The proposed algorithm extends the Bulut's algorithm for constructing scale-free networks with designated minimum degree k and maximum degree m, in such a way that: (1) it determines the ideal number of edges derived from the ideal degree distribution; and (2) after connecting each new node to k existing nodes as in the Bulut’s algorithm, it adjusts the number of edges to the ideal value by conducting add/removal of edges. We prove that such an adjustment is always possible if the number of nodes in the network exceeds [Formula: see text]. The performance of the algorithm is experimentally evaluated.

scholarly journals Concentration Properties of Extremal Parameters in Random Discrete Structures

2006 ◽  
Vol DMTCS Proceedings vol. AG,... (Proceedings) ◽  
Author(s):  
Michael Drmota
Keyword(s):  
Maximum Degree ◽  
Random Trees ◽  
Height Distribution ◽  
Exponential Tail ◽  
Scale Free ◽  
Main Emphasis ◽  
Discrete Structures ◽  
International Audience ◽  
Concentration Properties

International audience The purpose of this survey is to present recent results concerning concentration properties of extremal parameters of random discrete structures. A main emphasis is placed on the height and maximum degree of several kinds of random trees. We also provide exponential tail estimates for the height distribution of scale-free trees.

scholarly journals An Innovative Tour Recommendation System using Graph Algorithms

2019 ◽  
Vol 5 (1) ◽  
pp. 32 ◽  
Author(s):  
Md. Sabir Hossain ◽  
Ahsan Sadee Tanim ◽  
Nabila Nawal ◽  
Sharmin Akter
Keyword(s):  
Recommendation System ◽  
Search Algorithm ◽  
Limited Time ◽  
Dijkstra Algorithm ◽  
Breadth First Search ◽  
User Interest ◽  
Trip Planning ◽  
Physical Effort ◽  
Points Of Interest ◽  
Point Of Departure

Background: Tour recommendation and path planning are the most challenging jobs for tourists as they decide Points of Interest (POI).Objective: To reduce the physical effort of the tourists and recommend them a personalized tour is the main objective of this paper. Most of the time people had to find the places he wants to visit in a difficult way. It kills a lot of time.Methods: To cope with this situation we have used different methodology. First, a greedy algorithm is used for filtering the POIs and BFS (Breadth First Search) algorithm will find POI in terms of user interest. The maximum number of visited POI within a limited time will be considered. Then, the Dijkstra algorithm finds the shortest path from the point of departure to the end of tours.Results:  This work shows its users list of places according to the user's interest in a particular city. It also suggests them places to visit in a range from the location of the user where a user can dynamically change this range and it also suggests nearby places they may want to visit.Conclusion: This tour recommendation system provides its users with a better trip planning and thus makes their holidays enjoyable.

IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments

2020 ◽  
Vol 88 ◽  
pp. 105945 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Akshya Kaveri ◽  
Akshay Rahul K. ◽  
Kannan Krithivasan ◽  
...  
Keyword(s):  
Search Strategy ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cloud Environments ◽  
Gravitational Search

An Evolutionary Clustering-Based Optimization to Minimize Total Weighted Completion Time Variance in a Multiple Machine Manufacturing System

2015 ◽  
Vol 14 (05) ◽  
pp. 971-991 ◽  
Author(s):  
Hadi Mokhtari ◽  
Ali Salmasnia
Keyword(s):  
Manufacturing System ◽  
Search Strategy ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Total Weighted Completion Time ◽  
New Paradigm ◽  
Evolutionary Clustering ◽  
Processing Times ◽  
Proposed Model ◽  
Neighborhood Search Algorithm

This paper discusses clustering as a new paradigm of optimization and devises an integration of clustering and an evolutionary algorithm, neighborhood search algorithm (NSA), for a multiple machine system with the case of reducible processing times (RPT). After the problem is formulated mathematically, evolutionary clustering search (ECS) is devised to reach the near-optimal solutions. It is a way of detecting interesting search areas based on clustering. In this approach, an iterative clustering is carried out which is integrated to evolutionary mechanism NSA to identify which subspace is promising, and then the search strategy becomes more aggressive in detected areas. It is interesting to find out such subspaces as soon as possible to increase the algorithm's efficiency by changing the search strategy over possible promising regions. Once relevant search regions are discovered by clustering they can be treated with special intensification by the NSA algorithm. Furthermore, different neighborhood mechanisms are designed to be embedded within the main NSA algorithm so as to enhance its performance. The applicability of the proposed model and the performance of the NSA approach are demonstrated via computational experiments.

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