scholarly journals Estimation of the Effort Component of the Software Projects Using Heuristic Algorithms

10.5772/7583 ◽  
2010 ◽  
Author(s):  
Mitat Uysal
Keyword(s):  
Heuristic Algorithms ◽  
Software Projects

Editorial: controlling software projects

1986 ◽  
Vol 1 (1) ◽  
pp. 2
Author(s):  
B.A. Kitchenham
Keyword(s):  
Software Projects

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Segmentation of Brain Tumors using Meta Heuristic Algorithms

2014 ◽  
Vol 2014 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Jobin Christ ◽  
◽  
S. Sivagowri ◽  
Ganesh Babu ◽  
◽  
...  
Keyword(s):  
Brain Tumors ◽  
Heuristic Algorithms

Two Enhanced Heuristic Algorithms for the Minimum Initial Marking Problem of Petri Nets

2009 ◽  
Vol E92-A (11) ◽  
pp. 2732-2744
Author(s):  
Satoru OCHIIWA ◽  
Satoshi TAOKA ◽  
Masahiro YAMAUCHI ◽  
Toshimasa WATANABE
Keyword(s):  
Petri Nets ◽  
Heuristic Algorithms

Two Heuristic Algorithms for the Minimum Initial Marking Problem of Timed Petri Nets

2013 ◽  
Vol E96.A (2) ◽  
pp. 540-553
Author(s):  
Satoru OCHIIWA ◽  
Satoshi TAOKA ◽  
Masahiro YAMAUCHI ◽  
Toshimasa WATANABE
Keyword(s):  
Petri Nets ◽  
Heuristic Algorithms ◽  
Timed Petri Nets

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

2017 ◽  
Vol 7 (9) ◽  
pp. 30 ◽  
Author(s):  
Ramandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Heuristic Algorithms ◽  
Hybrid Approach ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Bee Colony ◽  
Effective Manner ◽  
Effective Performance

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

Investigation of the diversity index trend of the Search Group Algorithm in comparison with others meta-heuristic algorithms

2017 ◽  
Author(s):  
Rafael Souza ◽  
Leandro Fadel Miguel ◽  
Matheus Silva Gonçalves ◽  
Rafael Holdorf Lopez
Keyword(s):  
Heuristic Algorithms ◽  
Diversity Index

Heuristic Algorithms for Dynamic Spectrum Assignment in Open Spectrum System

2008 ◽  
Vol 19 (3) ◽  
pp. 479-491 ◽  
Author(s):  
Dan-Dan HAO
Keyword(s):  
Heuristic Algorithms ◽  
Dynamic Spectrum ◽  
Spectrum Assignment ◽  
Open Spectrum

Uncertainty and Coordination in Global Software Projects: A UK/India-Centred Case Study

2003 ◽  
Author(s):  
Su-Ying Lai ◽  
Richard Heeks ◽  
Brian Nicholson
Keyword(s):  
Software Projects

Heuristic Algorithms for Waste Load Allocation in a River Basin

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1057-1064 ◽  
Author(s):  
Vijay Joshi ◽  
Prasad Modak
Keyword(s):  
Dynamic Programming ◽  
Heuristic Algorithms ◽  
Water Quality Management ◽  
User Preferences ◽  
Theory And Practice ◽  
Waste Load Allocation ◽  
Practical Applications ◽  
Waste Load ◽  
On Line ◽  
The One

Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.

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