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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Journal ArticleDOI
Multi-Objective Optimization of Energy Consumption of GUIs in Android Apps
Mario Linares-Vasquez,Gabriele Bavota,Carlos Bernal-Cardenas,Massimiliano Di Penta,Rocco Oliveto,Denys Poshyvanyk +5 more
TL;DR: GEMMA, a tool aimed at optimizing the colors used by Android apps, is described, with the goal of reducing the energy consumption on (AM)OLED displays while keeping the user interface visually attractive for end-users.
Journal ArticleDOI
Better software analytics via “DUO”: Data mining algorithms using/used-by optimizers
TL;DR: It is possible, useful and necessary to combine data mining and optimization using DUO, and the era of papers that just use data miners is coming to an end.
Book ChapterDOI
Multi-objective Big Data Optimization with jMetal and Spark
TL;DR: The purpose is to study the influence of accessing data stored in the Hadoop File System HDFS in each evaluation step of a metaheuristic and to provide a software tool to solve multi-objective Big Data Optimization problems.
Journal ArticleDOI
Two-archive method for aggregation-based many-objective optimization
Lei Cai,Shiru Qu,Guojian Cheng +2 more
TL;DR: A novel two-archive method is proposed for solving many-objective optimization problems by exploiting the advantages of using two separate archives to balance the convergence and diversity and is extended by eliminating the restricted neighbourhood models.
Journal ArticleDOI
Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique
TL;DR: A data-aware module for the Hadoop eco-system is proposed and a distributed encoding technique for genetic algorithms efficient data processing is proposed to manage the distribution of data and its placement based on cluster analysis of the data itself.
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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