Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Memetic algorithms: a short introduction
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Algorithm 897: VTDIRECT95: Serial and parallel codes for the global optimization algorithm direct
ACM Transactions on Mathematical Software (TOMS)
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Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
High-performance numerical optimization on multicore clusters
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A novel parallel hybrid intelligence optimization algorithm for a function approximation problem
Computers & Mathematics with Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In this work we present the parallel implementation of a hybrid global optimization algorithm assembled specifically to tackle a class of time consuming interatomic potential fitting problems. The resulting objective function is characterized by large and varying execution times, discontinuity and lack of derivative information. The presented global optimization algorithm corresponds to an irregular, two-level execution task graph where tasks are spawned dynamically. We use the OpenMP tasking model to express the inherent parallelism of the algorithm on shared-memory systems and a runtime library which implements the execution environment for adaptive task-based parallelism on multicore clusters. We describe in detail the hybrid global optimization algorithm and various parallel implementation issues. The proposed methodology is then applied to a specific instance of the interatomic potential fitting problem for the metal titanium. Extensive numerical experiments indicate that the proposed algorithm achieves the best parallel performance. In addition, its serial implementation performs well and therefore can also be used as a general purpose optimization algorithm.