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Comparative Study of Recent Multimodal Evolutionary Algorithms

This page contains the complimentary material for the paper Comparative Study of Recent Multimodal Evolutionary Algorithms [5]. This includes the implementation of PNA-NSGA2 [1] (denoted MMNSGAII on the plots), MOBiDE [2] and NSGA2 [3] used to obtain the presented results, as well as the results obtained for all the functions from the CEC2013 benchmark [7]. The implementations were done in JAVA using ECJ [6] to implement the evolutionary algorithms.

All the results are computed with a precision of 1e-3.

Source code (zipped)

Resulting plots (zipped)

  [1] S. Bandaru and K. Deb,"A parameterless-niching-assisted bi-objective approach to multimodal optimization", in Evolutionary Computation (CEC), 2013 IEEE Congress on, June 2013, pp. 95–102.
  [2] A. Basak, S. Das, and K. Tan, "Multimodal optimization using a biobjective differential evolution algorithm enhanced with mean distance based selection", Evolutionary Computation, IEEE Transactions on, vol. 17, no. 5, pp. 666–685, Oct 2013.
  [3] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II", Trans. Evol. Comp, vol. 6, no. 2, pp. 182–197, Apr. 2002. [[|Available here]]
  [4] X. Li, A. Engelbrecht, and M. G. Epitropakis, "Benchmark functions for cec’2013 special session and competition on niching methods for multimodal function optimization", Evolutionary Computation and Machine Learning Group, RMIT University, Australia, Tech. Rep., 2013.
  [5] Pighetti, R., Pallez, D., & Precioso, F. (2015, December). "Comparative Study of Recent Multimodal Evolutionary Algorithms". In Computational Intelligence, 2015 IEEE Symposium Series on (pp. 837-844). IEEE.
  [6] "A java-based evolutionary computation research system, ecj",, accessed: 2016-09-19.
  [7] "Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization",
soft_multimodal.1474440887.txt.gz · Last modified: 2016/09/21 08:54 by Denis Pallez