IDEINFO: An Improved Vector-Weighted Optimization Algorithm

Abstract

This study proposes an improved vector-weighted averaging algorithm (IDEINFO) for the optimization of different problems. The original vector-weighted optimization algorithm (INFO) uses weighted averaging for entity structures and uses three core procedures to update the positions of the vectors. First, the update rule phase is based on the law of averaging and convergence acceleration to generate new vectors. Second, the vector combination phase combines the obtained vectors with the update rules to achieve a promising solution. Third, the local search phase helps the algorithm eliminate low-precision solutions and improve exploitability and convergence. However, this approach pseudo-randomly initializes candidate solutions, and therefore risks falling into local optima. We, therefore, optimize the initial distribution uniformity of potential solutions by using a two-stage backward learning strategy to initialize the candidate solutions, and a difference evolution strategy to perturb these vectors in the combination stage to produce improved candidate solutions. In the search phase, the search range of the algorithm is expanded according to the probability values combined with the t-distribution strategy, to improve the global search results. The IDEINFO algorithm is, therefore, a promising tool for optimal design based on the considerable efficiency of the algorithm in the case of optimization constraints.