The GLOBAL optimization method revisited

Abstract

The multistart clustering global optimization method called GLOBAL has been introduced in the 1980s for bound constrained global optimization problems with black-box type objective function. Since then the technological environment has been changed much. The present paper describes shortly the revisions and updates made on the involved algorithms to utilize the novel technologies, and to improve its reliability. We discuss in detail the results of the numerical comparison with the old version and with C-GRASP, a continuous version of the GRASP method. According to these findings, the new version of GLOBAL is both more reliable and more efficient than the old one, and it compares favorably with C-GRASP too.

Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots

“…For the unicycle it is convenient to use By defining atan2{0,0}=0, the above function remains continuous along any approaching direction to the goal. Therefore, the resulting command and are determined by the (9-10) using with (16)(17). In this section, the simulation results for a unicycle mobile robot among circular obstacles in a two-dimensional workspace are presented and then the robustness analysis and verification of the proposed algorithm for the unicycle mobile robot will be investigated in the next section.…”

“…The multistart clustering algorithm presented in this work is based on GLOBAL (Csendes, 1988), which is a modified version of the stochastic algorithm by Boender et al (1982) implemented in FORTRAN [16] . The GLOBAL method has two phases i.e.…”

“…Then, the local search is started from those points which have not been assigned to a cluster. GLOBAL uses the Single Linkage clustering rule [16] .…”

“…The new implementation GLOBALm, which has been written in MATLAB, is freely available for academic purposes [16] . It is the bound constrained global optimisation problems with black-box type objective function.…”

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

“…Then, the local search starts from those points which have not been assigned to a cluster. GLOBAL uses the Single Linkage clustering rule [17]. GLOBAL.m is the bound constrained global optimization problems with a black-box type objective function.…”