Journal ArticleDOI
jMetal: A Java framework for multi-objective optimization
Juan J. Durillo,Antonio J. Nebro +1 more
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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.About:
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.read more
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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.
Journal ArticleDOI
ALNS: a Python implementation of the adaptive large neighbourhood search metaheuristic
N. A. Wouda,Leon Lan +1 more
Book ChapterDOI
Comparison of Multi-objective Approaches to the Real-World Production Scheduling
Gregor Papa,Peter Korošec +1 more
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
Kalyanmoy Deb,Deb Kalyanmoy +1 more
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
Eckart Zitzler,Lothar Thiele +1 more
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
N. Srinivas,Kalyanmoy Deb +1 more
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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