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. 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: lien Results plot: lien

[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. [Online]. Available:
  http://dx.doi.org/10.1109/4235.996017

[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.
  [Online]. Available: http://goanna.cs.rmit.edu.au/~xiaodong/cec13-
  niching/competition/cec2013-niching-benchmark-tech-report.pdf

[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,”

  http://cs.gmu.edu/~eclab/projects/ecj/, accessed: 2016-09-19.