On the scalability of multi-objective metaheuristics for the software scheduling problem

Luna, Francisco; González-Álvarez, David L.; Chicano, Francisco; Vega‐Rodríguez, Miguel A.

doi:10.1109/isda.2011.6121807

Cited by 8 publications

(11 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under this formulation, employees cannot exceed their maximum total dedication to tasks given by the company's policy, but any amount of overwork within the company's rules is allowed. Later on, Luna et al [7] investigated a similar problem formulation in a multiobjective scenario where cost and completion time were not combined into a single fitness function. Their work also introduced a repair operator to improve finding feasible solutions.…”

Section: Eas For the Pspmentioning

confidence: 99%

“…In this work, we concentrate on the problem of optimally allocating people (employees) to activities (tasks) using automated approaches. Similar to previous work [5], [6], [7], we will refer to this problem as the project scheduling problem (PSP). Typical objectives to be optimized when making allocations are to minimize the cost and completion time of the software project.…”

Section: Introductionmentioning

confidence: 99%

“…Different EAs were adopted for different variants of the PSP, e.g., [5], [6], [17], [18], [19]. This also includes multiobjective variants of this problem [4], [7], [20], [21] as well as approaches using cooperative coevolution [22]. A comprehensive survey of related work was presented in the work by Di Penta et al [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis

Minku

Sudholt

Yao

2014

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Several variants of evolutionary algorithms (EAs) have been applied to solve the project scheduling problem (PSP), yet their performance highly depends on design choices for the EA. It is still unclear how and why different EAs perform differently. We present the first runtime analysis for the PSP, gaining insights into the performance of EAs on the PSP in general, and on specific instance classes that are easy or hard. Our theoretical analysis has practical implications-based on it, we derive an improved EA design. This includes normalizing employees' dedication for different tasks to ensure they are not working overtime; a fitness function that requires fewer pre-defined parameters and provides a clear gradient towards feasible solutions; and an improved representation and mutation operator. Both our theoretical and empirical results show that our design is very effective. Combining the use of normalization to a population gave the best results in our experiments, and normalization was a key component for the practical effectiveness of the new design. Not only does our paper offer a new and effective algorithm for the PSP, it also provides a rigorous theoretical analysis to explain the efficiency of the algorithm, especially for increasingly large projects.

show abstract

Section: Eas For the Pspmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis

Minku

Sudholt

Yao

2014

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…This is an improvement on the work of Alba and Chicano [1] since cost and duration are objectives that are in conflict, and combining them in a single objective function is not most appropriate method to tackle the problem. Luna et al [12,13] continued the research of Chicano et al [4] by studying the scalability performance of several multi-objective optimizers.…”

Section: The Software Project Scheduling Problemmentioning

confidence: 99%

A Multi-objective Genetic Algorithm for the Software Project Scheduling Problem

García-Nájera

Gómez-Fuentes

2014

Nature-Inspired Computation and Machine Learning

View full text Add to dashboard Cite

Abstract. The software project scheduling problem considers the assignment of employees to project tasks with the aim of minimizing the project cost and delivering the project on time. Recent research takes into account that each employee is proficient in some development tasks only, which requiere specific skills. However, this cannot be totally applied in the Mexican context due to software companies do not categorize their employees by software skills, but by their skill level instead. In this study we propose a model that is closer to how software companies operate in Mexico. Moreover, we propose a multi-objective genetic algorithm for solving benchmark instances of this model. Results show that our proposed genetic algorithm performs similarly to two recent approaches and that it finds better multi-objective solutions when they are compared to those found by a well-known multi-objective optimizer.

show abstract

“…Two quality indicators, the hypervolume (HV) [21] and the attainment surfaces [22], have been used to measure the quality of the resulting Pareto fronts. This piece of research has to be considered as an extension of a previous conference publication [23] and, as a consequence, we want to clearly state the novel contributions included here. They follow two different lines: on the one hand, we have included four new algorithms in the comparison (SPEA2, MOABC, MOCell, and GDE3, i.e., doubling the experimentation conducted) to provide the reader with insights about their scalability capabilities; on the other hand, we have thoroughly analized the solutions of the different algorithms to find correlations between their features and the region in the objective space in which these solutions are located.…”

Section: Introductionmentioning

confidence: 99%

The software project scheduling problem: A scalability analysis of multi-objective metaheuristics

Luna

González-Álvarez

Chicano

et al. 2014

Applied Soft Computing

Self Cite

View full text Add to dashboard Cite

Computer aided techniques for scheduling software projects is 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 multiobjective 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.

show abstract

On the scalability of multi-objective metaheuristics for the software scheduling problem

Cited by 8 publications

References 8 publications

Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis

Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis

A Multi-objective Genetic Algorithm for the Software Project Scheduling Problem

The software project scheduling problem: A scalability analysis of multi-objective metaheuristics

Contact Info

Product

Resources

About