Chaotic enhanced teaching-based differential evolution algorithm applied to discrete truss optimization

In this paper, we develop a discrete chaotic enhanced hybrid teaching-based differential evolution algorithm (CETDE) that can deal with realistic yet challenging constrained discrete truss design problems more efficiently. CETDE adaptively executes multiple mutation operators to quickly search for the solution, but at the same time, premature convergence, a common issue in discrete optimization, is also prevented by embedding the chaotic logistic rule into the mutations. An improved logical strategy that only allows potential mutants to go through structural analysis is introduced to reduce computation. An enhanced chaotic local search strategy and a one-component change technique are also developed for further efficiency. Besides, CETDE simultaneously keeps continuous and discrete population throughout the evolution, which is distinguishable from existing discrete optimizers, to preserve the stochastic nature. Compared to other state-of-the-art performers in the literature, CETDE illustrates both higher efficiency and solution optimality.