Those documents present the average number of optima detected by
MOBiDE[1] on all the function from the CEC2013 benchmark[2] using different
values for the parameter delta.
Those were computed for the study provided by Pighetti et al.[3].

Optima are detected with a precision of 1e-3.
Please refer to [3] and the source code for more details about parameters used and number of run performed.
All the results were obtained using the source code provided on the same web page as those plots.

[1] 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.

[2] 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

[3] 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.
