ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization

Liefooghe, Arnaud; Basseur, Matthieu; Jourdan, Laëtitia; Talbi, El‐Ghazali

doi:10.1007/978-3-540-70928-2_31

Cited by 64 publications

(38 citation statements)

References 16 publications

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“…The metaheuristics described in the previous section have all been implemented using the ParadisEO-MOEO library 1 [13]. ParadisEO-MOEO is a C++ whitebox object-oriented framework dedicated to the reusable design of metaheuristics for multi-objective optimization.…”

Section: Methodsmentioning

confidence: 99%

Metaheuristics for the Bi-objective Ring Star Problem

Liefooghe

Jourdan

Basseur

et al. 2008

Evolutionary Computation in Combinatorial Optimization

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Abstract. The bi-objective ring star problem aims to locate a cycle through a subset of nodes of a graph while optimizing two types of cost. The first criterion is to minimize a ring cost, related to the length of the cycle, whereas the second one is to minimize an assignment cost, from non-visited nodes to visited ones. In spite of its natural multi-objective formulation, this problem has never been investigated in such a way. In this paper, three metaheuristics are designed to approximate the whole set of efficient solutions for the problem under consideration. Computational experiments are performed on well-known benchmark test instances, and the proposed methods are rigorously compared to each other using different performance metrics.

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Section: Methodsmentioning

confidence: 99%

Metaheuristics for the Bi-objective Ring Star Problem

Liefooghe

Jourdan

Basseur

et al. 2008

Evolutionary Computation in Combinatorial Optimization

Self Cite

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“…Specifying input variables and reading results is done using input and output text files. To implement meta-heuristic algorithms, an efficient and adapted framework has been used (ParadisEO library [10]). OMOptim already includes several genetic algorithms, e.g.…”

Section: Methodsmentioning

confidence: 99%

Towards Design Optimization with OpenModelica Emphasizing Parameter Optimization with Genetic Algorithms

Thieriot¹,

Nemer²,

Torabzadeh-Tari³

et al. 2011

Linköping Electronic Conference Proceedings

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One of the main goals when modeling a physical system is to optimize its design or configuration. Currently existing platforms are often dependent on commercial software or are based on in-house and special-purpose development tools. These two alternatives present disadvantages that limit sharing and reusability. The same assessment has partly motivated the origin of the Modelica language itself. In this paper, a new optimization platform called OMOptim is presented. Intrinsically linked with OpenModelica, this platform is mainly aimed at facilitating optimization algorithm development, as well as application use together with models. A first version is already available and three test cases of which one using respectively Dymola and two using OpenModelica are presented. Future developments and design considerations of OMOptim but also of related OpenModelica computation functions are also discussed.

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“…(14). Although every library listed in Table 1 has its merits, jMetal (Durillo and Nebro 2011), PISA (Bleuler et al 2003) and ParadisEO (Liefooghe et al 2007) seem to be the most widely used by the community.…”

Section: Software Libraries For Population-based Optimizationmentioning

confidence: 99%

An overview of population-based algorithms for multi-objective optimisation

Giagkiozis

Purshouse

Fleming

2013

International Journal of Systems Science

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In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognized that population-based multi-objective optimization techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimization methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelizable and can generate an approximation of the entire Pareto front (PF) at each iteration. A critique of their capabilities is also provided.

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ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization

Cited by 64 publications

References 16 publications

Metaheuristics for the Bi-objective Ring Star Problem

Metaheuristics for the Bi-objective Ring Star Problem

Towards Design Optimization with OpenModelica Emphasizing Parameter Optimization with Genetic Algorithms

An overview of population-based algorithms for multi-objective optimisation

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