Search results

1 – 10 of 524
Content available
Article
Publication date: 27 November 2020

Petar Jackovich, Bruce Cox and Raymond R. Hill

This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and…

Abstract

Purpose

This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and vertex-greedy subclasses. As these subclasses of heuristics can create subtours, two known methodologies for subtour elimination on symmetric instances are reviewed and are expanded to cover asymmetric problem instances. This paper introduces a third novel subtour elimination methodology, the greedy tracker (GT), and compares it to both known methodologies.

Design/methodology/approach

Computational results for all three subtour elimination methodologies are generated across 17 symmetric instances ranging in size from 29 vertices to 5,934 vertices, as well as 9 asymmetric instances ranging in size from 17 to 443 vertices.

Findings

The results demonstrate the GT is the fastest method for preventing subtours for instances below 400 vertices. Additionally, a distinction between fragment constructive heuristics and the subtour elimination methodology used to ensure the feasibility of resulting solutions enables the introduction of a new vertex-greedy fragment heuristic called ordered greedy.

Originality/value

This research has two main contributions: first, it introduces a novel subtour elimination methodology. Second, the research introduces the concept of ordered lists which remaps the TSP into a new space with promising initial computational results.

Article
Publication date: 16 March 2020

Tianyi Wu, Jian Hua Liu, Shaoli Liu, Peng Jin, Hao Huang and Wei Liu

This paper aims to solve the problem of free-form tubes’ machining errors which are caused by their complex geometries and material properties.

Abstract

Purpose

This paper aims to solve the problem of free-form tubes’ machining errors which are caused by their complex geometries and material properties.

Design/methodology/approach

In this paper, the authors propose a multi-view vision-based method for measuring free-form tubes. The authors apply photogrammetry theory to construct the initial model and then optimize the model using an energy function. The energy function is based on the features of the image of the tube. Solving the energy function allows to use the gray features of the images to reconstruct centerline point clouds and thus obtain the pertinent geometric parameters.

Findings

According to the experiments, the measurement process takes less than 2 min and the precision of the proposed system is 0.2 mm. The authors used simple operations to carry out the measurements, and the process is fully automatic.

Originality/value

This paper proposes a method for measuring free-form tubes based on multi-view vision, which has not been attempted to the best of authors’ knowledge. This method differs from traditional multi-view vision measurement methods, because it does not rely on the data of the design model of the tube. The application of the energy function also avoids the problem of matching corresponding points and thus simplifying the calculation and improving its stability.

Details

Assembly Automation, vol. 40 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

Open Access
Article
Publication date: 15 March 2018

Moufida Maimour

Multipath routing holds a great potential to provide sufficient bandwidth to a plethora of applications in wireless sensor networks. In this paper, we consider the problem…

120

Abstract

Multipath routing holds a great potential to provide sufficient bandwidth to a plethora of applications in wireless sensor networks. In this paper, we consider the problem of interference that can significantly affect the expected performances. We focus on the performance evaluation of the iterative paths discovery approach as opposed to the traditional concurrent multipath routing. Five different variants of multipath protocols are simulated and evaluated using different performance metrics. We mainly show that the iterative approach allows better performances when used jointly with an interference-aware metric or when an interference-zone marking strategy is employed. This latter appears to exhibit the best performances in terms of success ratio, achieved throughput, control messages overhead as well as energy consumption.

Details

Applied Computing and Informatics, vol. 16 no. 1/2
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 3 July 2020

Mohammad Khalid Pandit, Roohie Naaz Mir and Mohammad Ahsan Chishti

The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The…

Abstract

Purpose

The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.

Design/methodology/approach

To realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.

Findings

Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.

Originality/value

The proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 8 July 2022

Mukesh Soni, Nihar Ranjan Nayak, Ashima Kalra, Sheshang Degadwala, Nikhil Kumar Singh and Shweta Singh

The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.

Abstract

Purpose

The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.

Design/methodology/approach

The new greedy algorithm is proposed to balance the energy consumption in edge computing.

Findings

The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.

Originality/value

The results are shown in this paper which are better as compared to existing algorithms.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 4 August 2022

Shikha Mehta

The social media revolution has brought tremendous change in business strategies for marketing and promoting the products and services. Online social networks have become…

Abstract

Purpose

The social media revolution has brought tremendous change in business strategies for marketing and promoting the products and services. Online social networks have become prime choice to promote the products because of the large size of online communities. Identification of seed nodes or identifying the users who are able to maximize the spread of information over the network is the key challenge faced by organizations. It is proved as non-deterministic polynomial-time hard problem. The purpose of this paper is to design an efficient algorithm for optimal seed selection to cover the online social network as much as possible to maximize the influence. In this approach, agglomerative clustering is used to generate the initial population of seed nodes for GA.

Design/methodology/approach

In this paper agglomerative clustering based approach is proposed to generate the initial population of seed nodes for GA. This approach helps in creating the initial populations of Genetic algorithm from different parts of the network. Genetic algorithm evolves this population and aids in generating the best seed nodes in the network.

Findings

The performance of of proposed approach is assessed with respect to existing seed selection approaches like k-medoid, k-means, general greedy, random, discounted degree and high degree. The algorithms are compared over networks data sets with varying out-degree ratio. Experiments reveal that the proposed approach is able to improve the spread of influence by 35% as compared to contemporary techniques.

Originality/value

This paper is original contribution. The agglomerative clustering-based GA for optimal seed selection is developed to improve the spread of influence in online social networks. This paper is of immense importance for viral marketing and the organizations willing to promote product or services online via influential personalities.

Details

International Journal of Web Information Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 1 December 2003

Dimos C. Charmpis and Manolis Papadrakakis

Balancing and dual domain decomposition methods (DDMs) comprise a family of efficient high performance solution approaches for a large number of problems in computational…

Abstract

Balancing and dual domain decomposition methods (DDMs) comprise a family of efficient high performance solution approaches for a large number of problems in computational mechanics. Such DDMs are used in practice on parallel computing environments with the number of generated subdomains being generally larger than the number of available processors. This paper presents an effective heuristic technique for organizing the subdomains into subdomain clusters, in order to assign each cluster to a processor. This task is handled by the proposed approach as a graph partitioning optimization problem using the publicly available software METIS. The objective of the optimization process is to minimize the communication requirements of the DDMs under the constraint of producing balanced processor workloads. This constraint optimization procedure for treating the subdomain cluster generation task leads to increased computational efficiencies for balancing and dual DDMs.

Details

Engineering Computations, vol. 20 no. 8
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 1 July 2014

Byung-Won On, Gyu Sang Choi and Soo-Mok Jung

The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs…

Abstract

Purpose

The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs) as a case study of the name authority control problem in DLs.

Design/methodology/approach

To find a sample of name variants across DLs (e.g. DBLP and ACM) and in a single DL (e.g. ACM), the approach is based on two bipartite matching algorithms: Maximum Weighted Bipartite Matching and Maximum Cardinality Bipartite Matching.

Findings

First, the authors validated the effectiveness and efficiency of the bipartite matching algorithms. The authors also studied the nature of real cases of author name variants that had been found across DLs (e.g. ACM, CiteSeer and DBLP) and in a single DL.

Originality/value

To the best of the authors knowledge, there is less research effort to understand the nature of author name variants shown in DLs. A thorough analysis can help focus research effort on real problems that arise when the authors perform duplicate detection methods.

Details

Program, vol. 48 no. 3
Type: Research Article
ISSN: 0033-0337

Keywords

Article
Publication date: 28 October 2014

Zhang-Hui Liu, Guo-Long Chen, Ning-Ning Wang and Biao Song

– The purpose of this paper is to present a new immunization strategy for effectively solving the control of the spread of the virus.

Abstract

Purpose

The purpose of this paper is to present a new immunization strategy for effectively solving the control of the spread of the virus.

Design/methodology/approach

Inspired by the idea of network partition, taking two optimization targets which are the scale of sub-network and the sum of the strengths of the sub-network's nodes into account at the same time, a new immunization strategy based on greedy algorithm in the scale-free network is presented. After specifying the number of nodes through the immunization, the network is divided into the scale of sub-network and the sum of the strength of the sub-network's nodes as small as possible.

Findings

The experimental results show that the proposed algorithm has the better performance than targeted immunization which is supposed to be highly efficient at present.

Originality/value

This paper proposes a new immunization strategy based on greedy algorithm in the scale-free network for effectively solving the control of the spread of the virus.

Details

Engineering Computations, vol. 31 no. 8
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 13 June 2016

Qingzheng Xu, Na Wang and Lei Wang

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum…

Abstract

Purpose

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum (COOBBO) algorithm.

Design/methodology/approach

The improvement measures tested in this paper include different initialization approaches, crossover approaches, local optimization approaches, and greedy approaches. Eight well-known traveling salesman problems (TSP) are employed for performance verification. Four comparison criteria are recoded and compared to analyze the contribution of each modified method.

Findings

Experiment results illustrate that the combination model of “25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy” may be the best choice of all when considering both the overall algorithm performance and computation overhead.

Originality/value

When solving TSP with varying scales, these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees. And an appropriate combination model may make the fullest possible contribution.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 9 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

1 – 10 of 524