ParadisEO-MOEO: Metaheuristics for Multiobjective Optimization

Paradiseo-MOEO is a general-purpose software framework dedicated to the design, implementation and analysis of metaheuristics for multiobjective optimization. It is based on a conceptual model that tends to unify a substantial number of state-of-the-art methodologies proposed so far. All those methods are here considered as simple variants of the same structure: a fine-grained decomposition following the main issues of fitness assignment, diversity preservation and elitism.

ParadisEO-MOEO is a white-box, object-oriented, C++, easy-to-use framework, portable across both Unix-like (Linux, MacOS) and Windows systems. It is governed by the CeCILL license. The framework embeds some features and techniques for a priori and a posteriori resolution methods and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. The rich set of ParadisEO-MOEO modular classes are combined to develop multiobjective metaheuristics. The related source code is maintained and regularly updated by the developers. Moreover, the framework perpetually evolves according to the needs and facilitates the development of new algorithms, either sequential or parallel, with a minimum effort. The ParadisEO-MOEO module is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers a maximum code and design reuse to the user. For instance, to solve your own real-coded problem by means of evolutionary multiobjective optimization, there is nothing to implement but the problem-specific evaluation function.

This makes from ParadisEO-MOEO a valuable tool for the scientific research community, the educational world and industrial organizations. The module tends to be used by both researchers and practitioners, non-specialists and experts, and has proven its validity and high flexibility by enabling the resolution of many academic, real-world and hard multiobjective optimization problems.

Summary of Features

  • Portability: Windows, Unix and MacOS, optionally on parallel and distributed architectures
  • Fine-grained decomposition
    • Dominance relations: Pareto-, weak-, strict-, epsilon-dominance
    • Fitness assignment schemes: scalar, dominance-based, indicator-based approaches
    • Diversity preservation mechanisms: sharing, crowding
    • Elitism: elitist selection, elitist replacement, archiving techniques (unbounded, bounded, fixed-size)
    • Statistical tools and quality indicators: hypervolume, additive and multiplicative epsilon, contribution, entropy
  • Easy-to-use state-of-the-art multiobjective metaheuristics (based on the aforementioned components)
    • Evolutionary algorithms: MOGA, NSGA, NSGA-II, SPEA2, IBEA, SEEA
    • Local search algorithms: PLS, IBMOLS, DMLS
    • Hybrid, parallel and distributed versions of the algorithms
  • Solution representation for multiobjective optimization problems of continuous and combinatorial nature
    • Real-coded, binary-strings, integer, permutation, user-defined representation
  • Multiobjective optimization problems: problem implementations to be plugged onto ParadisEO-MOEO are available for download as contributions
    • Benchmark continuous test functions: ZDT, DTLZ
    • Benchmark combinatorial problems: TSP, QAP, flowshop
    • Real-world problems: routing, scheduling, bioinformatics

Getting Started


If you are using ParadisEO-MOEO, please cite one of the following references:

Related publications:

  • A. Liefooghe, S. Mesmoudi, J. Humeau, L. Jourdan, E-G. Talbi. A Study on Dominance-Based Local Search Approaches for Multiobjective Combinatorial Optimization. Second International Workshop on Engineering Stochastic Local Search Algorithms: Designing, Implementing and Analyzing Effective Heuristics (SLS 2009), Lecture Notes in Computer Science (LNCS) vol. 5752, pp. 120-124, Brussels, Belgium, 2009
  • A. Liefooghe, L. Jourdan, E-G. Talbi. A Unified Model for Evolutionary Multi-objective Optimization and its Implementation in a General Purpose Software Framework. IEEE Symposium on Computational intelligence in Multi-Criteria Decision-Making (IEEE MCDM 2009), pp. 88-95, Nashville, Tennessee, USA, 2009
  • A. Liefooghe, M. Basseur, L. Jourdan, E-G. Talbi. ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization. Fourth International Conference on Evolutionary Multi-criterion Optimization (EMO 2007), Lecture Notes in Computer Science (LNCS) vol. 4403, pp. 386-400, Matsushima, Japan, 2007