scholarly journals Butterfly-core community search over labeled graphs

2021 ◽  
Vol 14 (11) ◽  
pp. 2006-2018
Author(s):  
Zheng Dong ◽  
Xin Huang ◽  
Guorui Yuan ◽  
Hengshu Zhu ◽  
Hui Xiong
Keyword(s):  
Case Studies ◽  
Heuristic Algorithm ◽  
Optimal Solution ◽  
Labeled Graph ◽  
Labeled Graphs ◽  
Community Search ◽  
Effectiveness And Efficiency ◽  
Connected Subgraphs

Community search aims at finding densely connected subgraphs for query vertices in a graph. While this task has been studied widely in the literature, most of the existing works only focus on finding homogeneous communities rather than heterogeneous communities with different labels. In this paper, we motivate a new problem of cross-group community search, namely Butterfly-Core Community (BCC), over a labeled graph, where each vertex has a label indicating its properties and an edge between two vertices indicates their cross relationship. Specifically, for two query vertices with different labels, we aim to find a densely connected cross community that contains two query vertices and consists of butterfly networks, where each wing of the butterflies is induced by a k-core search based on one query vertex and two wings are connected by these butterflies. We first develop a heuristic algorithm achieving 2-approximation to the optimal solution. Furthermore, we design fast techniques of query distance computations, leader pair identifications, and index-based BCC local explorations. Extensive experiments on seven real datasets and four useful case studies validate the effectiveness and efficiency of our BCC and its multi-labeled extension models.

Efficient size-bounded community search over large networks

2021 ◽  
Vol 14 (8) ◽  
pp. 1441-1453
Author(s):  
Kai Yao ◽  
Lijun Chang
Keyword(s):  
Heuristic Algorithm ◽  
State Of The Art ◽  
Minimum Degree ◽  
Exact Algorithms ◽  
Edge Density ◽  
Search Problem ◽  
Community Search ◽  
Connected Subgraphs ◽  
General Size ◽  
Baseline Approach

The problem of community search, which aims to find a cohesive subgraph containing user-given query vertices, has been extensively studied recently. Most of the existing studies mainly focus on the cohesiveness of the returned community, while ignoring the size of the community, and may yield communities of very large sizes. However, many applications naturally require that the number of vertices/members in a community should fall within a certain range. In this paper, we design exact algorithms for the general size-bounded community search problem that aims to find a subgraph with the largest min-degree among all connected subgraphs that contain the query vertex q and have at least l and at most h vertices, where q, l, h are specified by the query. As the problem is NP-hard, we propose a branch-reduce-and-bound algorithm SC-BRB by developing nontrivial reducing techniques, upper bounding techniques, and branching techniques. Experiments on large real graphs show that SC-BRB on average increases the minimum degree of the community returned by the state-of-the-art heuristic algorithm GreedyF by a factor of 2.41 and increases the edge density by a factor of 2.2. In addition, SC-BRB is several orders of magnitude faster than a baseline approach, and all of our proposed techniques contribute to the efficiency of SC-BRB.

Pareto domain: an invaluable source of process information

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Geraldine Cáceres Sepúlveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Case Studies ◽  
Optimal Solution ◽  
Pareto Optimal ◽  
Process Variables ◽  
Optimal Point ◽  
Decision Variables ◽  
Visualization Tools ◽  
Pareto Domain ◽  
And Performance ◽  
Typical Process

AbstractDue to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives.

The Location Problem of Given and Indivisible Different Units

1970 ◽  
Vol 2 (3) ◽  
pp. 341-356
Author(s):  
G. Jándy
Keyword(s):  
Exact Solution ◽  
Heuristic Algorithm ◽  
Network Flow ◽  
Location Problem ◽  
Optimal Solution ◽  
Type Problem ◽  
Distribution Problem ◽  
Enumeration Algorithm ◽  
Weighted Distribution ◽  
Partial Enumeration

In cases where certain simplifications are allowed, the location optimisation of given and indivisible different economic units may be modelled as a bi-value weighted distribution problem. The paper presents a heuristic algorithm for this network-flow-type problem and also a partial enumeration algorithm for deriving the exact solution. But it is also pointed out that an initial sub-optimal solution can quickly be improved with a derivation on a direct line only, if the exact solution is not absolutely essential. A numerical example is used to illustrate the method of derivation on a direct line starting with an upper bound given by a sub-optimal solution.

scholarly journals Enumerating the Number of Connected Vertices Labeled Graph of Order Six with Maximum Ten Loops and Containing No Parallel Edges

2020 ◽  
Vol 5 (4) ◽  
pp. 131
Author(s):  
Wamiliana Wamiliana ◽  
Amanto Amanto ◽  
Mustofa Usman ◽  
Muslim Ansori ◽  
Fadila Cahya Puri
Keyword(s):  
Connected Graph ◽  
Labeled Graph ◽  
Labeled Graphs

A Graph G (V, E) is said to be a connected graph if for every two vertices on the graph there exist at least a path connecting them, otherwise, the graph is disconnected. Two edges or more that connect the same pair of vertices are called parallel edges, and an edge that starts and ends at the same vertex is called a loop.  A graph is called simple if it containing no loops nor parallel edges. Given n vertices and m edges, m ≥ 1, there are many graphs that can be formed, either connected or disconnected. In this research, we will discuss how to calculate the number of connected vertices labeled graphs of order six (isomorphism graphs are counted as one), with a maximum loop of ten without parallel edges.  

Heuristic algorithm for optimal redundancy allocation in complex systems

2019 ◽  
Vol 25 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Sudhanshu Aggarwal
Keyword(s):  
Complex Systems ◽  
Heuristic Algorithm ◽  
Communication Systems ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Optimal Solution ◽  
Systems Design ◽  
Computational Time ◽  
Coherent System ◽  
Content Type

PurposeThe purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.Design/methodology/approachThe algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.FindingsThe proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.Research limitations/implicationsIn general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.Practical implicationsIt is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.Social implicationsReliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.Originality/valueThe utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

scholarly journals Robust Satellite Scheduling Approach for Dynamic Emergency Tasks

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Xuejun Zhai ◽  
Xiaonan Niu ◽  
Hong Tang ◽  
Lixin Wu ◽  
Yonglin Shen
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithm ◽  
Optimal Solution ◽  
Simulation Experiments ◽  
Multiobjective Genetic Algorithm ◽  
Scheduling Model ◽  
Rapid Responses ◽  
Earth Observation Satellites ◽  
Satellite Scheduling ◽  
Combined Algorithm

Earth observation satellites play a significant role in rapid responses to emergent events on the Earth’s surface, for example, earthquakes. In this paper, we propose a robust satellite scheduling model to address a sequence of emergency tasks, in which both the profit and robustness of the schedule are simultaneously maximized in each stage. Both the multiobjective genetic algorithm NSGA2 and rule-based heuristic algorithm are employed to obtain solutions of the model. NSGA2 is used to obtain a flexible and highly robust initial schedule. When every set of emergency tasks arrives, a combined algorithm called HA-NSGA2 is used to adjust the initial schedule. The heuristic algorithm (HA) is designed to insert these tasks dynamically to the waiting queue of the initial schedule. Then the multiobjective genetic algorithm NSGA2 is employed to find the optimal solution that has maximum revenue and robustness. Meanwhile, to improve the revenue and resource utilization, we adopt a compact task merging strategy considering the duration of task execution in the heuristic algorithm. Several experiments are used to evaluate the performance of HA-NSGA2. All simulation experiments show that the performance of HA-NSGA2 is significantly improved.

Direct Gravitational Search Algorithm for Global Optimisation Problems

2016 ◽  
Vol 6 (3) ◽  
pp. 290-313 ◽  
Author(s):  
Ahmed F. Ali ◽  
Mohamed A. Tawhid
Keyword(s):  
Heuristic Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Direct Method ◽  
Gravitational Search Algorithm ◽  
Optimal Solution ◽  
Slow Convergence ◽  
Gravitational Search ◽  
Law Of Gravity ◽  
Number Of Iterations

AbstractA gravitational search algorithm (GSA) is a meta-heuristic development that is modelled on the Newtonian law of gravity and mass interaction. Here we propose a new hybrid algorithm called the Direct Gravitational Search Algorithm (DGSA), which combines a GSA that can perform a wide exploration and deep exploitation with the Nelder-Mead method, as a promising direct method capable of an intensification search. The main drawback of a meta-heuristic algorithm is slow convergence, but in our DGSA the standard GSA is run for a number of iterations before the best solution obtained is passed to the Nelder-Mead method to refine it and avoid running iterations that provide negligible further improvement. We test the DGSA on 7 benchmark integer functions and 10 benchmark minimax functions to compare the performance against 9 other algorithms, and the numerical results show the optimal or near optimal solution is obtained faster.

Initial attempt to optimize the operation of a hydrometric network using branch and bound algorithm

1988 ◽  
Vol 15 (4) ◽  
pp. 717-725 ◽  
Author(s):  
Patrice M. Pelletier ◽  
Slobodan P. Simonovic
Keyword(s):  
Heuristic Algorithm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Traveling Salesman ◽  
Future Research ◽  
Initial Attempt ◽  
Research Directions ◽  
New Approach ◽  
Future Research Directions ◽  
Field Area

The paper presents a new approach developed to formulate and solve the problem of field operation of a hydrometric network. In present practice, optimization techniques are not used for solving this problem. In the initial attempt, documented in the paper, the traveling salesman algorithm is used to find the optimal solution. Assumptions used and experience gained during the research are addressed in the paper. For the Dauphin hydrometric field area in Manitoba, an "optimal" solution has been obtained and presented. Based on the analysis of the results, conclusions were drawn about future research directions incorporating development of an effective heuristic algorithm for solving the traveling hydrometric technician problem. Key words: operation, hydrometric network, optimization, heuristic algorithm, traveling salesman algorithm.

Cross Warehouse Scheduling of Logistics Distribution and Cycle Re-Claimer Based on Heuristics Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2606-2610 ◽  
Author(s):  
Rong Hua Ma
Keyword(s):  
Heuristic Algorithm ◽  
Programming Model ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Simulation Method ◽  
New Method ◽  
Logistics Distribution ◽  
Suboptimal Solution ◽  
Taboo Search Algorithm ◽  
Heuristics Algorithm

In the uncertain environment, the cycle re-claimer and logistics distribution cross warehouse scheduling is important and it should be optimized. The mathematical programming model is constructed, and the proposed two-stage heuristic algorithm is proposed, the optimal solution of heuristic algorithm is used as the initial value. And the taboo search algorithm is designed to improve the initial solution. In order to verify the availability of the method, the Monte Carlo simulation method is used for numerical experiment. The experiment results show that the new method can solve the suboptimal solution which close to the optimal solution in fast, and the taboo searching algorithm can improve the solution of heuristic algorithm, it has significantly improvement performance for new method, and it has good application value in practice.

Research on Second Distribution of Real Value Detectors Based on Heuristic Movement

2014 ◽  
Vol 1049-1050 ◽  
pp. 1506-1513
Author(s):  
Tian Bo Wang ◽  
Feng Bin Zhang ◽  
Chun He Xia
Keyword(s):  
Theoretical Analysis ◽  
Anomaly Detection ◽  
Heuristic Algorithm ◽  
Negative Selection ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Generalization Ability ◽  
Real Value ◽  
Omission Rate

Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. However, there still remain many problems such as the randomness of detector generation, incompleteness of self-set and the generalization ability of detectors, which would cause a lot of loopholes in non-self-space. This paper proposes a heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self-space in the first distribution. The algorithm proposed can distribute real value detectors through omission data based on the methods of partition and movement. A method is then proposed to solve the problem on how to get the optimal solution to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that the algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.

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