Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Net Practice for Software Project Management
IEEE Software
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Software project management with GAs
Information Sciences: an International Journal
Staffing a software project: A constraint satisfaction and optimization-based approach
Computers and Operations Research
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
MOCell: A cellular genetic algorithm for multiobjective optimization
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Software—Practice & Experience
Using multi-objective metaheuristics to solve the software project scheduling problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
Computer aided techniques for scheduling software projects are a crucial step in the software development process within the highly competitive software industry. The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime, thus involving mainly both people-intensive activities and human resources. Two major conflicting goals arise when scheduling a software project: reducing both its cost and duration. A multi-objective approach is therefore the natural way of facing the SPS problem. As companies are getting involved in larger and larger software projects, there is an actual need of algorithms that are able to deal with the tremendous search spaces imposed. In this paper, we analyze the scalability of eight multi-objective algorithms when they are applied to the SPS problem using instances of increasing size. The algorithms are classical algorithms from the literature (NSGA-II, PAES, and SPEA2) and recent proposals (DEPT, MOCell, MOABC, MO-FA, and GDE3). From the experimentation conducted, the results suggest that PAES is the algorithm with the best scalability features.