Journal ArticleDOI
jMetal: A Java framework for multi-objective optimization
Juan J. Durillo,Antonio J. Nebro +1 more
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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.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
Cloud — Edge Offloading Model for Vehicular Traffic Analysis
Dragi Kimovski,Dijana Capeska Bogatinoska,Narges Mehran,Aleksandar Karadimce,Natasa Paunkoska,Radu Prodan,Ninoslav Marina +6 more
TL;DR: In this article, a model for scheduling of vehicular traffic analysis applications with partial task offloading across the cloud/edge continuum is presented, which represents the traffic applications as a set of interconnected tasks composed into a workflow that can be partially offloaded to the edge.
Dissertation
Multi-Objective Optimization of Solar Thermal Combisystems
TL;DR: The proposed multi-objective optimization framework uses a generic solar combisystem model coupled with the micro-TVMOPSO algorithm to find designs reducing the life cycle cost, life cycle energy use, and life cycle exergy destroyed of solar thermal combisSystems.
Journal ArticleDOI
MOSA/D: Multi-operator evolutionary many-objective algorithm with self-adaptation of parameters based on decomposition
TL;DR: An improved evolutionary many-objective optimization (EMaO) method, MOSA/D, which is a Multi-Operator approach with Self-Adaptation of parameters based on D ecomposition, is proposed.
Journal ArticleDOI
MetaNChemo: A meta-heuristic neural-based framework for chemometric analysis
TL;DR: MetaNChemo is proposed, a multidisciplinary end-to-end framework to build a chemometric system from the very production of chemoresistive sensors, through the data sampling and pre-processing phases necessary to calibrate such sensors, to the identification and the assessment of the most suitable artificial intelligence models able to supports the detection of the concentration of a target gas in the air.
Journal ArticleDOI
GAME: GPU accelerated multipurpose evolutionary algorithm library
TL;DR: The genetic algorithm library discussed in this article is the first that contains fully parallelised GPU implementations of multi-objective genetic algorithms besides the single-objectives ones, which provides flexible and efficient GPU accelerated GAs.
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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