Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
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Book ChapterDOI
Metco: a parallel plugin-based framework for multi-objective optimization
TL;DR: In this paper, the authors present a parallel framework for the solution of multi-objective optimization problems, which implements some of the best known multiobjective evolutionary algorithms, and makes use of configuration files to provide a more extensive and simple customization environment than other similar tools.
Journal ArticleDOI
Study of self-stopping PDMOSA and performance measure in multiobjective optimization
TL;DR: With the help of metrics methods, it has been found that the quality, extent and diversification of a Pareto set of solutions produced by PDMOSA-I is better than that produced by SMOSA.
Journal ArticleDOI
Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization
Guo Yu,Yaochu Jin,Markus Olhofer +2 more
TL;DR: The proposed test problems offer a new means to develop and assess preference-based evolutionary algorithms for solving multi- and many-objective optimization problems, and new performance indicators are suggested for evaluating the capability of optimization algorithms in locating the knee points.
Journal ArticleDOI
SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines
Jianmei Guo,Jia Hui Liang,Kai Shi,Dingyu Yang,Jingsong Zhang,Krzysztof Czarnecki,Vijay Ganesh,Huiqun Yu +7 more
TL;DR: A hybrid multi-objective optimization algorithm called SMTIBEA that combines the indicator-based evolutionary algorithm (IBEA) with the satisfiability modulo theories (SMT) solving is proposed that significantly extends the expressiveness of constraints and simultaneously achieves a comparable performance.
Journal ArticleDOI
A novel non-dominated sorting algorithm for evolutionary multi-objective optimization
TL;DR: HNDS first sorts all candidate solutions in ascending order by their first objective, then compares the first solution with all others one by one to make a rapid distinction between different quality solutions, thereby avoiding many unnecessary comparisons.