Generalized TRUST Algorithms for Global Optimization

The TRUST methodology addresses the unconstrained global optimization problem in terms of the evolution of a novel deterministic nonlinear dynamical system, which combines the concepts of subenergy tunneling and non-Lipschitzian “terminal” repellers. In this paper, the TRUST algorithms are generalized by extending the formalism to lower semicontinuous objective functions, and by allowing gradient-directed tunneling with componentwise flow direction reversal at the boundaries of the parameter domain. Known limitations of the methodology are summarized, and the reduction of a multi-dimensional problem to a one-dimensional case (e.g., via hyperspiral embedding) is discussed with regards to a formal convergence proof. Benchmark results are presented, which demonstrate that TRUST is substantially faster than previously published global optimization techniques.

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