Computational geometry: an introduction
Computational geometry: an introduction
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Graphics (TOG)
Global Optimization on Funneling Landscapes
Journal of Global Optimization
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching: Similarity Measures and Algorithms
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
On the multilevel structure of global optimization problems
Computational Optimization and Applications
A Population-based Approach for Hard Global Optimization Problems based on Dissimilarity Measures
Mathematical Programming: Series A and B
Global Optimization of Morse Clusters by Potential Energy Transformations
INFORMS Journal on Computing
New results for molecular formation under pairwise potential minimization
Computational Optimization and Applications
An experimental analysis of a population based approach for global optimization
Computational Optimization and Applications
Efficiently packing unequal disks in a circle
Operations Research Letters
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Very hard optimization problems, i.e., problems with a large number of variables and local minima, have been effectively attacked with algorithms which mix local searches with heuristic procedures in order to widely explore the search space. A Population Based Approach based on a Monotonic Basin Hopping optimization algorithm has turned out to be very effective for this kind of problems. In the resulting algorithm, called Population Basin Hopping, a key role is played by a dissimilarity measure. The basic idea is to maintain a sufficient dissimilarity gap among the individuals in the population in order to explore a wide part of the solution space.The aim of this paper is to study and computationally compare different dissimilarity measures to be used in the field of Molecular Cluster Optimization, exploring different possibilities fitting with the problem characteristics. Several dissimilarities, mainly based on pairwise distances between cluster elements, are introduced and tested. Each dissimilarity measure is defined as a distance between cluster descriptors, which are suitable representations of cluster information which can be extracted during the optimization process.It will be shown that, although there is no single dissimilarity measure which dominates the others, from one side it is extremely beneficial to introduce dissimilarities and from another side it is possible to identify a group of dissimilarity criteria which guarantees the best performance.