It is shown that the performance of HBFA with Uniform distribution is very sensitive to the dimension of the problem, since the efficiency is good when n is small but gets worse when n is large.
For the results of Table 5, we use HBFA based on mCS, with both "erf" function in (14), together with (10), and the floor function (15).
Hence, we aim to analyze the effects of two factors "A" and "B." "A" is the HBFA implementation (with two levels) and "B" is the problem's dimension (with three levels).
Based on the abovementioned parameters, the HBFA with mCS based on (14) was run 30 times and the averaged results were the following.
In this work we have implemented several heuristics to compute a global optimal binary solution of bound constrained nonlinear optimization problems, which have been incorporated into FA, yielding the herein called HBFA. The problems addressed in this study have bounded continuous search space.