scholarly journals Software Quality Assesment using COCOMO-II Metrics with ABC and NN

2020 ◽  
Vol 9 (3) ◽  
pp. 2982-2988
Keyword(s):  
Cost Model ◽  
Small Variation ◽  
Effort Estimation ◽  
Quality Estimation ◽  
Bee Colony ◽  
Proposed Model ◽  
Software Development System ◽  
Model Author ◽  
Key Aspects ◽  
Cocomo Ii

Time, cost and quality predictions are the key aspects of any software development system. Loses that result due to wrong estimations may lead to irresistible damage. It is observed that a badly estimated project always results into a bad quality output as the efforts are put in the wrong direction. In the present study, author proposed ABC-COCOMO-II as a new model and tried to enhance the extent of accuracy in effort quality assessment through effort estimation. In the proposed model author combined the strengths of COCOMO-II (Constructive Cost Model) with the Artificial Bee Colony (ABC) and Neural Network (NN). In the present work, ABC algorithm is used to select the best solution, NN is used for the classification purpose to improve the quality estimation using COCOMO-II. The results are compared and evaluated with the pre-existing effort estimation models. The simulation results had shown that the proposed combination outperformed in terms of quality estimation with small variation of 5-10% in comparison to the actual effort, which further leads to betterment of the quality. More than 90% projects results into high quality output for the proposed algorithmic architecture.

Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy

2021 ◽  
Vol 9 (2) ◽  
pp. 139
Author(s):  
Alifia Puspaningrum ◽  
Fachrul Pralienka Bani Muhammad ◽  
Esti Mulyani
Keyword(s):  
Search Algorithm ◽  
Cost Model ◽  
Cuckoo Search ◽  
Estimation Accuracy ◽  
Flower Pollination Algorithm ◽  
Metaheuristic Algorithm ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Flower Pollination ◽  
Cocomo Ii

Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value.  In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.

scholarly journals Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tinggui Chen ◽  
Shiwen Wu ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Sudden Onset ◽  
The Road ◽  
Bee Colony ◽  
Proposed Model ◽  
Effectiveness And Efficiency ◽  
In Transit ◽  
Emergency Supply

It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

Feeder Bus Timetable Design and Vehicle Size Setting in Peak Hour Demand Conditions

2019 ◽  
Vol 2673 (10) ◽  
pp. 321-332 ◽  
Author(s):  
Xueping Dou ◽  
Qiang Meng
Keyword(s):  
Mixed Integer ◽  
Arrival Times ◽  
Non Linear Programming ◽  
Bee Colony ◽  
Proposed Model ◽  
Feeder Bus ◽  
Minlp Model ◽  
Failure Cost ◽  
Set Up ◽  
The Given

This study proposes a solution to the feeder bus timetabling problem, in which the terminal departure times and vehicle sizes are simultaneously determined based on the given transfer passengers and their arrival times at a bus terminal. The problem is formulated as a mixed integer non-linear programming (MINLP) model with the objective of minimizing the transfer waiting time of served passengers, the transfer failure cost of non-served passengers, and the operating costs of bus companies. In addition to train passengers who plan to transfer to buses, local passengers who intend to board buses are considered and treated as passengers from virtual trains in the proposed model. Passenger attitudes and behaviors toward the waiting queue caused by bus capacity constraints in peak hour demand conditions are explicitly embedded in the MINLP model. A hybrid artificial bee colony (ABC) algorithm is developed to solve the MINLP model. Various experiments are set up to account for the performance of the proposed model and solution algorithm.

Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

2021 ◽  
pp. 1203-1221
Author(s):  
Shivlal Mewada ◽  
Sita Sharan Gautam ◽  
Pradeep Sharma
Keyword(s):  
Privacy Preservation ◽  
Artificial Bee Colony ◽  
Model Simulation ◽  
Privacy Preserving ◽  
Global Network ◽  
Sensitive Information ◽  
Healthcare Applications ◽  
Efficient Manner ◽  
Bee Colony ◽  
Proposed Model

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

Halton Based Initial Distribution in Artificial Bee Colony Algorithm and its Application in Software Effort Estimation

2012 ◽  
Vol 3 (2) ◽  
pp. 86-106 ◽  
Author(s):  
Tarun Kumar Sharma ◽  
Millie Pant
Keyword(s):  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Real Life ◽  
Initial Distribution ◽  
Benchmark Problems ◽  
Model Parameters ◽  
Random Numbers ◽  
Initial Population ◽  
Effort Estimation ◽  
Bee Colony

Artificial Bee Colony (ABC) is an optimization algorithm that simulates the foraging behavior of honey bees. It is a population based search technique whose performance depends largely on the distribution of initial population. Generally, uniform distributions are preferred since they best reflect the lack of knowledge about the optimum’s location. Moreover, these are easy to generate as most of the programming languages have an inbuilt function for generating uniformly distributed random numbers. However, in case of a population dependent optimization algorithm like that of ABC, random numbers having uniform probability distribution may not be a good choice as they may not be able exploit the search space fully. This paper uses quasi random numbers based on Halton sequence for the initial distribution and have compared the simulation results with initial population generated using uniform distribution. The proposed variant, termed as Halton based ABC (H-ABC), is validated on a set of 15 standard benchmark problems, 6 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and has been used for solving the real life problem of estimating the cost model parameters. Numerical results indicate the competence of the proposed algorithm.

Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment

2020 ◽  
Vol 11 (3) ◽  
pp. 22-41
Author(s):  
Akkrabani Bharani Pradeep Kumar ◽  
P. Venkata Nageswara Rao
Keyword(s):  
Energy Consumption ◽  
Electrical Power ◽  
Service Level Agreement ◽  
Service Level ◽  
Management Approach ◽  
Cloud Data ◽  
Proposed Model ◽  
Key Aspects ◽  
Cloud Infrastructures ◽  
Resource Aware

Over the past few decades, computing environments have progressed from a single-user milieu to highly parallel supercomputing environments, network of workstations (NoWs) and distributed systems, to more recently popular systems like grids and clouds. Due to its great advantage of providing large computational capacity at low costs, cloud infrastructures can be employed as a very effective tool, but due to its dynamic nature and heterogeneity, cloud resources consuming enormous amount of electrical power and energy consumption control becomes a major issue in cloud datacenters. This article proposes a comprehensive prediction-based virtual machine management approach that aims to reduce energy consumption by reducing active physical servers in cloud data centers. The proposed model focuses on three key aspects of resource management namely, prediction-based delay provisioning; prediction-based migration, and resource-aware live migration. The comprehensive model minimizes energy consumption without violating the service level agreement and provides the required quality of service. The experiments to validate the efficacy of the proposed model are carried out on a simulated environment, with varying server and user applications and parameter sizes.

scholarly journals An IoT Smart Broiler Farming Model for Low Income Farmers

2018 ◽  
Vol 6 (3) ◽  
pp. 95
Author(s):  
Hazael Phiri ◽  
Douglas Kunda ◽  
Jackson Phiri
Keyword(s):  
Low Income ◽  
Cost Model ◽  
Low Cost ◽  
Ease Of Use ◽  
Climate Control ◽  
Proposed Model ◽  
Zigbee Protocol ◽  
Gsm Network ◽  
The Cost ◽  
Broiler House

<p class="Abstract"><span lang="EN-US">The coming of Internet of things (IoT) brings opportunities for the deploying of wireless sensor networks. One area of deployment is smart poultry farming to improve the quality and security of chicken varieties that include broilers. The quality of broilers produced is dependent on the environment in which the broilers are kept. In addition, the revenue of the farmer is guaranteed if theft of stock is prevented. The current methods farmers use are labour intensive and time consuming as they are manual. Leveraging the features of IoT and sensors can help to monitor the environment and ensure adverse conditions are reported for farmers to take action before they harm the livestock. Incorporating intruder detection when monitoring conditions in the environment can also prevent stock theft and that can increase the income obtained by farmers. For such a system to be widely adopted by low income farmers, the cost should be low compared commercially available climate control systems that are meant for commercial farmers. The system should also provide ease of use for less technically skilled farmers, reduce the time taken by farmers to take action in controlling theft and conditions in the environment and be accessible from any location other than the broiler house. In this paper, we propose a low-cost model that can be used to monitor conditions in the environment of a broiler house and send the values to the farmer in real-time. The proposed model is based on open source microcontrollers, ZigBee protocol, GSM network, mobile applications and cloud computing. </span></p>

Applicability of the software cost model COCOMO II to HPC projects

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Matthias Meinke ◽  
Matthias S. Müller ◽  
Michael Schlottke Lakemper ◽  
Sandra Wienke ◽  
Julian Miller
Keyword(s):  
Cost Model ◽  
Software Cost ◽  
Cocomo Ii

scholarly journals Presentation of Multi-Skill Workforce Scheduling Model and Solving the Model Using Meta-Heuristic Algorithms

2016 ◽  
Vol 10 (2) ◽  
pp. 194
Author(s):  
Iman Fozveh ◽  
Hooman Salehi ◽  
Kamran Mogharehabed
Keyword(s):  
Heuristic Algorithms ◽  
Night Shift ◽  
Workforce Scheduling ◽  
Multi Objective ◽  
Bee Colony ◽  
De Algorithm ◽  
Scheduling Model ◽  
Proposed Model ◽  
Bee Colony Optimization ◽  
Quality Dispersion

<span lang="EN-US">In the present article, a multi-objective mathematical model for scheduling multi-skilled multi-objective workforce has been proposed with the aims of minimizing the number of night-shift engineers, minimizing the total cost of workforce and maximizing the number of engaged workforce. To solve the proposed model for scheduling workforce, bee colony optimization algorithm and DE algorithm have been employed, and in order to investigate the efficiency of these two algorithms, the results have been compared with each other in terms of quality, dispersion and uniformity factors. In order to solve the model three sample problems (40, 70 and 280 workforce) were designed and then solved by the two mentioned algorithms. Bee algorithm is able to find higher-quality answers. Also the results of the comparison of dispersion and uniformity index indicate that bee colony algorithm is able to find answers with more dispersion and more homogeneous than DE algorithm. The comparison of solution time of both algorithms indicate that bee colony algorithm is faster than DE algorithm and needs less time to reach quality, dispersed and homogenous answers.</span>

Sign in / Sign up

Export Citation Format

Share Document