Keane’s bump function is considered as a standard benchmark for nonlinear constrained optimization. It is highly multi-modal and its optimum is located at the non-linear constrained boundary. The true minimum of this function is, perhaps, unknown. We intend in this paper to optimize Keane’s function of different dimensions (2 to 100) by the Repulsive Particle Swarm and Differential Evolution methods. The DE optimization program has gone a long way to obtain the optimum results. However, the Repulsive Particle Swarm optimization has faltered. We have also conjectured that the values of the decision variables diminish with the increasing index values and they form two distinct clusters with almost equal number of members. These regularities indicate whether the function could attain a minimum or (at least) has reached close to the minimum. We have used this conjecture to incorporate ordering of variable values before evalution of the function and its optimization at every trial. As a result, the performance of DE as well as the RPS has improved significantly. Our results are comparable with the best results available in the literature on optimization of Keane function. Our two findings are notable: (i) Keane’s envisaged min(f) = -0.835 for 50-dimensional problem is realizable; (ii) Liu-Lewis’ min(f) = -0.84421 for 200-dimensional problem is grossly sub-optimal.Computer programs (written by us in Fortran) are available on request.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
file. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number
3098.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.: