On Uncensored Mean First-Passage-Time Performance Experiments with Multi-Walk: a New Stochastic Optimization Algorithm

A rigorous empirical comparison of two stochastic solvers is important when one of the solvers is a prototype of a new algorithm such as multi-walk (MWA). When searching for global minima in IR , the key data structures of MWA include: p rulers with each ruler assigned m marks and a set of p neigh/nu-1mod matrices of size up to m*(m-2), where each entry represents absolute values of paénvise differences between m marks. Before taking the next step, a controller links the tableau of neighborhood matrices and computes new and improved positions for each of the m marks. The number of columns in each neighborhood matrix is denoted as the neighborhood radius r <;= m - 2. Any variant of the DEA (differential evolution algorithm) has an effective population neighborhood of radius not larger than 1. Uncensored first-passage-time performance experiments that vary the neighborhood radius of a MW-solver can thus be readily compared to existing variants of UK-solvers. This paper considers seven test cases of increasing complexity and demonstrates, under uncensored first-passage-time performance experiments: (1) significant variability in convergence rate fir seven UE-hased solver configurations, and (2) consistent. monotonic. and significantly faster rate of convergence for the MW-solver prototype as we increase the neighborhood radius from 4 to its maximum value.