A Population-based Approach for Hard Global Optimization Problems based on Dissimilarity Measures

摘要

When dealing with extremely hard global optimization problems, i.e. problems with a large number of variables and a huge number of local optima, heuristic procedures are the only possible choice. In this situation, lacking any possibility of guaranteeing global optimality for most problem instances, it is quite difficult to establish rules for discriminating among different algorithms. We think that in order to judge the quality of new global optimization methods, different criteria might be adopted like, e.g.: efficiency – measured in terms of the computational effort necessary to obtain the putative global optimum