An experimental analysis of a population based approach for global optimization
Computational Optimization and Applications
Solving molecular distance geometry problems by global optimization algorithms
Computational Optimization and Applications
Global optimization of binary Lennard-Jones clusters
Optimization Methods & Software - GLOBAL OPTIMIZATION
Dissimilarity measures for population-based global optimization algorithms
Computational Optimization and Applications
Solving the problem of packing equal and unequal circles in a circular container
Journal of Global Optimization
Machine learning for global optimization
Computational Optimization and Applications
Efficiently packing unequal disks in a circle
Operations Research Letters
DACCO: a discrete ant colony algorithm to cluster geometry optimization
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Packing unequal circles using formulation space search
Computers and Operations Research
Self-adaptive mate choice for cluster geometry optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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 robustness – measured in terms of “percentage of successes”, i.e. of the number of times the algorithm, re-started with different seeds or starting points, is able to end up at the putative global optimum discovery capability – measured in terms of the possibility that an algorithm discovers, for the first time, a putative optimum for a given problem which is better than the best known up to now.