Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Journal of Global Optimization
Multistart algorithms for seeking feasibility
Computers and Operations Research
Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization
INFORMS Journal on Computing
Optimization Methods & Software - GLOBAL OPTIMIZATION
Pseudo-Cut Strategies for Global Optimization
International Journal of Applied Metaheuristic Computing
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We present results of extensive computational tests of (i) comparing dynamic filters (first mentioned in an earlier publication addressing a feasibility seeking algorithm) with static filters and (ii) stochastic starting point generators ('drivers') for a multi-start global optimization algorithm called MSNLP (Multi-Start Non-Linear Programming). We show how the widely used NLP local solvers CONOPT and SNOPT compare when used in this context. Our computational tests utilize two large and diverse sets of test problems. Best known solutions to most of the problems are obtained competitively, within 30 solver calls, and the best solutions are often located in the first ten calls. The results show that the addition of dynamic filters and new global drivers can contribute to the increased reliability of the MSNLP algorithmic framework.