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Redesigning the jMetal Multi-Objective Optimization Framework
2015
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15
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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
2019
Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19
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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]
2018
Advances in Intelligent Systems and Computing
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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]
2019
arXiv
pre-print
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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]
2010
Studies in Computational Intelligence
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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
2016
2016 International Conference on High Performance Computing & Simulation (HPCS)
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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]
2009
arXiv
pre-print
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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
2021
Computation
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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
2011
European Journal of Operational Research
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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
2020
IEEE Access
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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
2017
Applied Soft Computing
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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
2018
Applied Soft Computing
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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
2014
Journal of Cleaner Production
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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
2017
International Journal of Neural Systems
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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
2011
Applied Soft Computing
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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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