Multi-objective optimization approach based on Minimum Population Search algorithm

• Darian Reyes-Fernández-de-Bulnes ^[1] ; Antonio Bolufé-Röhler ^[2] ; Dania Tamayo-Vera ^[3]
  1. [1] Instituto Tecnológico de Tijuana

     Instituto Tecnológico de Tijuana

     México

     
  2. [2] University of Prince Edward Island

     University of Prince Edward Island

     Canadá

     
  3. [3] Thinking Big Inc., Canada

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• Localización: GECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología, ISSN-e 2255-5684, Vol. 7, Nº. 2, 2019, págs. 1-19
• Idioma: inglés
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• Resumen

  • Minimum Population Search is a recently developed metaheuristic for optimization of mono-objective continuous problems, which has proven to be a very effective optimizing large scale and multi-modal problems. One of its key characteristic is the ability to perform an efficient exploration of large dimensional spaces. We assume that this feature may prove useful when optimizing multi-objective problems, thus this paper presents a study of how it can be adapted to a multi-objective approach. We performed experiments and comparisons with five multi-objective selection processes and we test the effectiveness of Thresheld Convergence on this class of problems. Following this analysis we suggest a Multi-objective variant of the algorithm. The proposed algorithm is compared with multi-objective evolutionary algorithms IBEA, NSGA2 and SPEA2 on several well-known test problems. Subsequently, we present two hybrid approaches with the IBEA and NSGA-II, these hybrids allow to further improve the achieved results.

• Referencias bibliográficas
  • Citas Bader, J., (2010). Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods. Ph.D. dissertation, ETH Zurich, Switzerland.
  • Bleuler, S., M. Laumanns, L. Thiele and E. Zitzler (2003). PISA - A platform and programming language independent interface for search algorithms....
  • Bolufé-Röhler, A. and S. Chen (2013) Minimum Population Search - Lessons from building a heuristic technique with two population members....
  • Bolufé-Röhler, A., S. Estévez-Velarde, A. Piad-Morffis, S. Chen and J. Montgomery (2013). Differential evolution with thresheld convergence....
  • Bolufé-Röhler, A., A. Coto-Santiesteban, M. Rosa-Soto and S. Chen (2014). Minimum Population Search, an Application to Molecular Docking....
  • Bolufé-Röhler, A. and S. Chen (2014). Extending Minimum Population Search towards Large Scale Global Optimization. IEEE Congress on Evolutionary...
  • Bolufé-Röhler, A., S. Fiol-Gonzalez and S. Chen (2015). A Minimum Population Search Hybrid for Large Scale Global Optimization. IEEE Congress...
  • Chen, S., Montgomery, J., Bolufé-Röhler, A. and Gonzalez-Fernandez, Y. (2015). A Review of Thresheld Convergence. Revista GECONTEC 3(1).
  • Conover, W. J. (1999). Practical Nonparametric Statistics, 3rd ed. John Wiley & Sons.
  • Deb, K. (2001) Multi-Objective Optimization using Evolutionary Algorithms. New York, EE.UU.: Wiley.
  • Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions Evolutionary...
  • Deb, K., L. Thiele, M. Laumanns, and Zitzler, E. (2005) Scalable test problems for evolutionary multi-objective optimization. Evolutionary...
  • Deb, K. (2011). Multi-Objective Optimization Using Evolutionary Algorithms: An Introduction. Indian Institute of Technology Kanpur, India,...
  • Deb, K., and H. Jain (2014). An Evolutionary Many-Objective OptimizationAlgorithm Using Reference-point Based Non-dominated Sorting Approach,...
  • Fonseca C. M. and P. J. Fleming (1993). Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. Proceedings...
  • Glorieux E., B. Svensson, F. Danielsson, and B. Lennartson (2017). Constructive cooperative coevolution for large-scale global optimisation....
  • Hansen M. P. and A. Jaszkiewicz (1999). Evaluating the Quality of Approximations to the Non-Dominated Set. Technical University of Denmark,...
  • Knowles J. D., R. A. Watson, and D. W. Corne (2001). Reducing local optima in single objective problems by multi-objectivization. 1st International...
  • Piad-Morffis A., S. Estévez-Velarde, A. Bolufé-Röhler, J. Montgomery and S. Chen (2015). Evolution strategies with thresheld convergence....
  • Talbi G. (2009). Metaheuristics: From design to implementation, 1st ed. John Wiley & Sons.
  • Tamayo-Vera, D., Bolufé-Röhler, A. and Chen, S. (2016). Estimation multi-variate normal algorithm with thresheld convergence. Evolutionary...
  • Zitzler, E. and L. Thiele (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE...
  • Zitzler, E., M. Laumanns, and L. Thiele (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computer Engineering and Networks...
  • Zitzler, E., L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. da Fonseca (2003). Performance assessment of multiobjective optimizers: an analysis...
  • Zitzler, E. K. Deb and L. Thiele (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation,...
  • Zitzler, E. and S. Kunzli (2004). Indicator-based selection in multi-objective search. Proceedings of the 8th International Conference on...