A multi-start global minimization algorithm with dynamic search trajectories
Journal of Optimization Theory and Applications
Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Algorithm 667: Sigma—a stochastic-integration global minimization algorithm
ACM Transactions on Mathematical Software (TOMS)
Global optimization
A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Brief paper: Redundancy versus multiple starting points in nonlinear system related inverse problems
Automatica (Journal of IFAC)
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The unconstrained global programming problem is addressed using an efficient multi-start algorithm, in which parallel local searches contribute towards a Bayesian global stopping criterion. The stopping criterion, denoted the unified Bayesian global stopping criterion, is based on the mild assumption that the probability of convergence to the global optimum x* is comparable to the probability of convergence to any local minimum xj. The combination of the simple multi-start local search strategy and the unified Bayesian global stopping criterion outperforms a number of leading global optimization algorithms, for both serial and parallel implementations. Results for parallel clusters of up to 128 machines are presented.