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
Citations
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Journal ArticleDOI
Multi-Objective Reconstruction of Software Architecture
TL;DR: This paper describes the performance evaluation of a number of metaheuristic search algorithms applied to architecture reconstruction problems with high dimensionality in terms of objectives and informs the ongoing development of a software tool that supports the analysis of trade-offs between different reconstructed architectures.
Journal ArticleDOI
Do Performance Aspirations Matter for Guiding Software Configuration Tuning? An Empirical Investigation under Dual Performance Objectives
TL;DR: In this paper , the authors present a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation.
Proceedings ArticleDOI
Optimization of assignment of tasks to teams using multi-objective metaheuristics
TL;DR: This paper addresses the Assignment Problem as a bi-objective optimization problem, in which the cost is minimized and the quality is maximized, and adopts Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2).
Book ChapterDOI
An Effective Method for MOGAs Initialization to Solve the Multi-Objective Next Release Problem
TL;DR: The experimental results have shown that the Path Relinking based method is consistently superior to the random initialization method and the extreme solutions method, considering the convergence speed and the quality of the Pareto front, that was measured using the Spread and Hypervolume indexes.
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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Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
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