This article presents a novel investigation on two real value methods Free Search and Particle Swarm Optimization applied to global optimization numerical tests. The objective is to identify how to facilitate assessment of heuristic, evolutionary, adaptive and other optimization and search algorithms. Particular aim is to measure: (1) probability for success of given method; (2) abilities of given method for entire search space coverage; (3) dependence on initialization; (4) abilities of given method to escape from trapping in local sub-optima. Achieved experimental results are presented and analyzed.
No takes yet. Share an insight, caveat, or question.
Vasileva et al. (2012) studied this question.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: