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Journal ArticleDOI

jMetal: A Java framework for multi-objective optimization

Juan J. Durillo, +1 more
- 01 Oct 2011 - 
- Vol. 42, Iss: 10, pp 760-771
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TLDR
This paper describes jMetal, an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving multi-objective optimization problems, and includes two case studies to illustrate the use of jMetal in both solving a problem with a metaheuristic and designing and performing an experimental study.
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This article is published in Advances in Engineering Software.The article was published on 2011-10-01. It has received 1025 citations till now. The article focuses on the topics: Metaheuristic & Multi-objective optimization.

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Citations
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Proceedings ArticleDOI

Multi-objective fitness-proportional attraction approach with weights

TL;DR: The performance of F-PAW is compared to three well-known multi-objective algorithms through an experiment on 16 common test problems taken from the WFG and DTLZ benchmarks, and results indicate two conclusions.
Book ChapterDOI

Comparison of Multi-objective Approaches to the Real-World Production Scheduling

TL;DR: It is shown that the multi-objective approaches are able to find high-quality solutions, also when quick respond is required to adapt to dynamic business conditions.
Proceedings ArticleDOI

Multi-Objective Metamorphic Test Case Selection: an Industrial Case Study (Practical Experience Report)

TL;DR: In this article , an evolutionary multi-objective approach for the metamorphic test case selection problem is presented, adapting existing multiobjective test selection techniques and proposing new evolutionary operators and objective functions.
Proceedings ArticleDOI

Distributed execution of test cases and continuous integration

TL;DR: The preliminary results suggest that the found solutions are worthwhile as compared to a traditional non-distributed TS execution (i.e., a single server/PC) and it seems that the solutions are worth to speed up the testing activities in the context of CI.
References
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Journal ArticleDOI

A fast and elitist multiobjective genetic algorithm: NSGA-II

TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Book

Multi-Objective Optimization Using Evolutionary Algorithms

TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Journal ArticleDOI

Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach

TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Journal ArticleDOI

Muiltiobjective optimization using nondominated sorting in genetic algorithms

TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
Book

Evolutionary algorithms for solving multi-objective problems

TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
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