Parallel two-level simulated annealing
ICS '93 Proceedings of the 7th international conference on Supercomputing
Global Optima of Lennard-Jones Clusters
Journal of Global Optimization
A recursive random search algorithm for large-scale network parameter configuration
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Efficient Algorithms for Large Scale Global Optimization: Lennard-Jones Clusters
Computational Optimization and Applications
On the multilevel structure of global optimization problems
Computational Optimization and Applications
A Trust-Region Algorithm for Global Optimization
Computational Optimization and Applications
Global Optimization of Morse Clusters by Potential Energy Transformations
INFORMS Journal on Computing
A new class of test functions for global optimization
Journal of Global Optimization
An experimental analysis of a population based approach for global optimization
Computational Optimization and Applications
The Impact of Global Structure on Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Large-scale network parameter configuration using an on-line simulation framework
IEEE/ACM Transactions on Networking (TON)
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
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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
A memetic multi-agent collaborative search for space trajectory optimisation
International Journal of Bio-Inspired Computation
A heuristic approach for packing identical rectangles in convex regions
Computers and Operations Research
Using a private desktop grid system for accelerating drug discovery
Future Generation Computer Systems
A global optimization method for the design of space trajectories
Computational Optimization and Applications
SIAM Journal on Scientific Computing
Machine learning for global optimization
Computational Optimization and Applications
Efficiently packing unequal disks in a circle
Operations Research Letters
Energy landscapes of atomic clusters as black box optimization benchmarks
Evolutionary Computation
Packing unequal circles using formulation space search
Computers and Operations Research
Advances in Artificial Intelligence - Special issue on Artificial Intelligence Applications in Biomedicine
Differential evolution methods based on local searches
Computers and Operations Research
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Molecular conformation problems arising in computational chemistry require the global minimization of a non-convex potential energy function representing the interactions of, for example, the component atoms in a molecular system. Typically the number of local minima on the potential energy surface grows exponentially with system size, and often becomes enormous even for relatively modestly sized systems. Thus the simple multistart strategy of randomly sampling local minima becomes impractical. However, for many molecular conformation potential energy surfaces the local minima can be organized by a simple adjacency relation into a single or at most a small number of funnels. A distinguished local minimum lies at the bottom of each funnel and a monotonically descending sequence of adjacent local minima connects every local minimum in the funnel with the funnel bottom. Thus the global minimum can be found among the comparatively small number of funnel bottoms, and a multistart strategy based on sampling funnel bottoms becomes viable. In this paper we present such an algorithm of the basin-hopping type and apply it to the Lennard–Jones cluster problem, an intensely studied molecular conformation problem which has become a benchmark for global optimization algorithms. Results of numerical experiments are presented which confirm both the multifunneling character of the Lennard–Jones potential surface as well as the efficiency of the algorithm. The algorithm has found all of the current putative global minima in the literature up to 110 atoms, as well as discovered a new global minimum for the 98-atom cluster of a novel geometrical class.