Optimizing Time and Effort Parameters of COCOMO II using Fuzzy Multi-Objective Particle Swarm Optimization

Estimating the efforts, costs, and schedules of software projects is a frequent challenge to software development projects. A bad estimation will result in bad management of a project. Various models of estimation have been defined to complete this estimate. The Constructive Cost Model II (COCOMO II) is one of the most famous models as a model for estimating efforts, costs, and schedules. To estimate the effort, cost, and schedule in project of software, the COCOMO II uses inputs: Effort Multiplier (EM), Scale Factor (SF), and Source Line of Code (SLOC). Evidently, this model is still lack in terms of accuracy rates in both efforts estimated and time of development. In this paper, we introduced to use Gaussian Membership Function (GMF) of Fuzzy Logic and Multi-Objective Particle Swarm Optimization (MOPSO) method to calibrate and optimize the parameters of COCOMO II. It is to achieve a new level of accuracy better on COCOMO II. The Nasa93 dataset is used to implement the method proposed. The experimental results of the method proposed have reduced the error downto 11.89% and 8.08% compared to the original COCOMO II. This method proposed has achieved better results than previous studies

Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method

Estimating the effort and cost of software is an important activity for software project managers. A poor estimate (overestimates or underestimates) will result in poor software project management. To handle this problem, many researchers have proposed various models for estimating software cost. Constructive Cost Model II (COCOMO II) is one of the best known and widely used models for estimating software costs. To estimate the cost of a software project, the COCOMO II model uses software size, cost drivers, scale factors as inputs. However, this model is still lacking in terms of accuracy. To improve the accuracy of COCOMO II model, this study examines the effect of the cost factor and scale factor in improving the accuracy of effort estimation. In this study, we initialized using Particle Swarm Optimization (PSO) to optimize the parameters in a model of COCOMO II. The method proposed is implemented using the Turkish Software Industry dataset which has 12 data items. The method can handle improper and uncertain inputs efficiently, as well as improves the reliability of software effort. The experiment results by MMRE were 34.1939%, indicating better high accuracy and significantly minimizing error 698.9461% and 104.876%

Optimizing Effort and Time Parameters of COCOMO II Estimation using Fuzzy Multi-objective PSO

The  estimation  of  software  effort  is  an  essential and  crucial   activity   for  the  software   development   life  cycle. Software effort estimation is a challenge that often appears on the project of making a software. A poor estimate will produce result in a worse project management.  Various software cost estimation model has been introduced  to resolve this problem. Constructive Cost Model II (COCOMO II Model) create large extent most considerable  and broadly  used as model  for cost estimation.  To estimate   the  effort  and  the  development   time  of  a  software project,  COCOMO  II model uses cost drivers,  scale factors  and line  of  code.  However,  the  model  is  still  lacking  in  terms  of accuracy both in effort and development  time estimation.  In this study,   we   do   investigate   the   influence   of   components   and attributes to achieve new better accuracy improvement on COCOMO II model. And we introduced the use of Gaussian Membership  Function  (GMF)  Fuzzy  Logic  and Multi-Objective Particle Swarm Optimization method (MOPSO) algorithms in calibrating  and optimizing  the COCOMO  II model parameters. The   proposed   method   is   applied   on   Nasa93   dataset.   The experiment  result of proposed method able to reduce error down to  11.891%  and  8.082%  from  the  perspective  of  COCOMO  II model.  The  method  has  achieved  better  results  than  those  of previous   researches   and  deals  proficient   with  inexplicit   data input and further improve reliability of the estimation method

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively