Multistart is a celebrated global optimization technique frequently applied in practice. In its
pure form, multistart has low efficiency. However, the simplicity of multistart and multitude of …

In this paper the problem of stopping the Multistart algorithm for global optimization is
considered. The algorithm consists of repeatedly performing local searches from randomly …

Global optimization finds applications in a wide range of real world problems. The multi-start
methods are a popular class of global optimization techniques, which are based on the idea …

We present three new stopping rules for Multistart based methods. The first uses a device
that enables the determination of the coverage of the bounded search domain. The second …

In this paper a sequential stopping rule is developed for the Multistart algorithm. A statistical
model for the values of the observed local maxima of an objective function is introduced in …

By far the most efficient methods for global optimization are based on starting a local
optimization routine from an appropriate subset of uniformly distributed starting points. As …

We describe and test 2 types of improvements to the multistart heuristic framework for
smooth global optimization presented in [8]. This starts a gradient based local NLP solver …

A stochastic global optimization method based on Multistart is presented. In this, the local
search is conditionally applied with a probability that takes in account the topology of the …

We develop new Markov chain Monte Carlo samplers for neighborhood generation in global
optimization algorithms based on Hit-and-Run. The success of Hit-and-Run as a sampler on …

One of the most commonly encountered approaches for the solution of unconstrained global
optimization problems is the application of multi-start algorithms. These algorithms usually …