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Redesigning the jMetal Multi-Objective Optimization Framework

Antonio J. Nebro, Juan J. Durillo, Matthieu Vergne
2015 Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15  
jMetal, an open source, Java-based framework for multiobjective optimization with metaheuristics, has become a valuable tool for many researches in the area as well as for some industrial partners in the  ...  This paper revisits the jMetal architecture, describing its refined new design, which relies on design patterns, principles from object-oriented design, and a better use of the Java language features to  ...  As a result, the redesigned jMetal should lead to a significantly improved tool aimed at being useful to researchers of the multi-objective optimization community.  ... 
doi:10.1145/2739482.2768462 dblp:conf/gecco/NebroDV15 fatcat:olze2vtqefbzhkuvjbvfmyyc6u

Automatic configuration of NSGA-II with jMetal and irace

Antonio J. Nebro, Manuel López-Ibáñez, Cristóbal Barba-González, José García-Nieto
2019 Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19  
jMetal is a Java-based framework for multi-objective optimization with metaheuristics providing, among other features, a wide set of algorithms that are representative of the state-of-the-art.  ...  Our proposal involves the definition of a new algorithm template for evolutionary algorithms, which allows the flexible composition of multi-objective evolutionary algorithms from a set of configurable  ...  Since the emergence of PISA [2] in 2003, many frameworks have been proposed, being jMetal one of them. jMetal started in 2006 [9] as a research project to develop a Java-based software framework for multi-objective  ... 
doi:10.1145/3319619.3326832 dblp:conf/gecco/NebroLBG19 fatcat:kunla2zffve7bnmxmkgytbolre

On the Integrity of Performance Comparison for Evolutionary Multi-objective Optimisation Algorithms [chapter]

Kevin Wilson, Shahin Rostami
2018 Advances in Intelligent Systems and Computing  
This paper proposes the notion that the experimental results and performance analyses of newly developed algorithms in the field of multi-objective optimisation may not offer sufficient integrity for hypothesis  ...  This is demonstrated through the multiple comparison of three implementations of the popular Non-dominated Sorting Genetic Algorithm II (NSGA-II) from well-regarded frameworks using the hypervolume indicator  ...  Like jMetal, it also contains implementations of a large group of multi-objective optimization algorithms, standard test problems and performance indicators.  ... 
doi:10.1007/978-3-319-97982-3_1 fatcat:googmwghqvhl7nwedyg4bjde4e

jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics [article]

Antonio Benitez-Hidalgo, Antonio J. Nebro, Jose Garcia-Nieto, Izaskun Oregi, Javier Del Ser
2019 arXiv   pre-print
Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming  ...  This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques.  ...  Javier Del Ser and Izaskun Oregui receive funding support from the Basque Government through the EMAITEK Program. Bibliography  ... 
arXiv:1903.02915v2 fatcat:dfia5jxdobctvbnpeaagk5qk64

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization [chapter]

Arnaud Liefooghe, Laetitia Jourdan, Thomas Legrand, Jérémie Humeau, El-Ghazali Talbi
2010 Studies in Computational Intelligence  
This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization.  ...  This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics.  ...  This work was supported by the ANR DOCK project.  ... 
doi:10.1007/978-3-642-11218-8_5 fatcat:crnmim6wfjcffbg7lykhjfrzii

A parallel cooperative coevolutionary SMPSO algorithm for multi-objective optimization

Arash Atashpendar, Bernabe Dorronsoro, Gregoire Danoy, Pascal Bouvry
2016 2016 International Conference on High Performance Computing & Simulation (HPCS)  
This paper presents a new parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm.  ...  In such an architecture, the population is split into several subpopulations, which are in turn in charge of optimizing a subset of the global solution by using the original multi-objective algorithm.  ...  ACKNOWLEDGEMENT Bernabé Dorronsoro would like to acknowledge the Spanish MINECO for the support provided under contracts TIN2014-60844-R (the SAVANT project) and RYC-2013-13355.  ... 
doi:10.1109/hpcsim.2016.7568405 dblp:conf/ieeehpcs/AtashpendarDDB16 fatcat:tst6nv46l5aifpbubyw3fh5efa

A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO [article]

Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi (LIFL, INRIA Lille - Nord Europe)
2009 arXiv   pre-print
The presented model is then incorporated into a general-purpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO.  ...  This package has proven its validity and flexibility by enabling the resolution of many real-world and hard multiobjective optimization problems.  ...  Sébastien Cahon and Nouredine Melab for their work on the preliminary version of the ParadisEO-MOEO software framework presented in this paper.  ... 
arXiv:0904.2987v1 fatcat:t55bm53m7neatksnhoygiekgqe

An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models

George Tsakalidis, Kostas Georgoulakos, Dimitris Paganias, Kostas Vergidis
2021 Computation  
The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs  ...  The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working  ...  Task Composition and Optimization of Process Designs This work is built upon the evolutionary multi-objective optimization framework for business process designs (bpo F ) presented in [5] and elaborated  ... 
doi:10.3390/computation9020016 fatcat:monhyz33tzdfddrygfiduog2uq

A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO

Arnaud Liefooghe, Laetitia Jourdan, El-Ghazali Talbi
2011 European Journal of Operational Research  
framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO.  ...  This framework has proven its validity and high flexibility by enabling the resolution of many academic, real-world and hard multiobjective optimization problems.  ...  and Nouredine Melab for their work on the preliminary version of the ParadisEO-MOEO software framework presented in this paper.  ... 
doi:10.1016/j.ejor.2010.07.023 fatcat:zrunuizuerfmhckhowaic5j43a

Extractive Multi-Document Arabic Text Summarization using Evolutionary Multi-Objective Optimization with K-medoid Clustering

Rana Alqaisi, Wasel Ghanem, Aziz Qaroush
2020 IEEE Access  
Optimization algorithms divided into single-objective and multi-objectives optimization.  ...  On the other hand, in Multi-objective optimization more than one objective function are optimized simultaneously.  ... 
doi:10.1109/access.2020.3046494 fatcat:cpt4q3ryxzfqlitlgixvgwqu5q

A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem

Giovani Guizzo, Silvia R. Vergilio, Aurora T.R. Pozo, Gian M. Fritsche
2017 Applied Soft Computing  
The field of Search-Based Software Engineering (SBSE) has widely utilized Multi-Objective Evolutionary Algorithms (MOEAs) to solve complex software engineering problems.  ...  Choice Function and Multi-Armed Bandit.  ...  HITO was implemented using jMetal [17] , an object-oriented framework for within the hyper-heuristics field for comparison [4, 28, 36, 47] .  ... 
doi:10.1016/j.asoc.2017.03.012 fatcat:pxxngvfy2vci7bo6qcifoxgbw4

A many-objective evolutionary algorithm based on rotated grid

Juan Zou, Liuwei Fu, Jinhua Zheng, Shengxiang Yang, Guo Yu, Yaru Hu
2018 Applied Soft Computing  
Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs).  ...  The algorithm uses the rotating grid to partition the objective space.  ...  The authors wish to thank the support of the National Natural Science Foun-  ... 
doi:10.1016/j.asoc.2018.02.031 fatcat:kd54qvtnz5dqjhnew4wjco4fsa

An investigation into minimising total energy consumption and total weighted tardiness in job shops

Ying Liu, Haibo Dong, Niels Lohse, Sanja Petrovic, Nabil Gindy
2014 Journal of Cleaner Production  
Therefore, a multi-objective scheduling method is developed in this paper with reducing energy consumption as one of the objectives.  ...  A model for the bi-objectives problem that minimises total electricity consumption and total weighted tardiness is developed and the Non-dominant Sorting Genetic Algorithm is employed as the solution to  ...  The algorithm had been developed based on the Jmetal framework (Nebro and Durillo, 2011) .  ... 
doi:10.1016/j.jclepro.2013.07.060 fatcat:zlfulqh4kjaqno3ji5ukzpkqrq

A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets

Alberto Fernández, Cristobal José Carmona, María José del Jesus, Francisco Herrera
2017 International Journal of Neural Systems  
For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline  ...  The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers.  ...  Acknowledgments This work was supported by the Spanish Ministry of Science and Technology under projects TIN2014-57251-P and TIN2015-68454-R; the Andalusian Research Plan P11-TIC-7765; and both the University  ... 
doi:10.1142/s0129065717500289 pmid:28633551 fatcat:3avzppodxrgptmymycxcwlzkqa

Machine scheduling in custom furniture industry through neuro-evolutionary hybridization

Juan C. Vidal, Manuel Mucientes, Alberto Bugarín, Manuel Lama
2011 Applied Soft Computing  
The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii)  ...  Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron neural networks.  ...  NSGA-II multi-objective approach for machine scheduling A multi-objective scheduling problem can be described as a multi-objective optimization problem: minF(x) = {f 1 (x), f 2 (x), . . . , f k (x)}s.t  ... 
doi:10.1016/j.asoc.2010.04.020 fatcat:tnp4hnercbeh3ebngngfq63cxu
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