A Group Rotate-Vector Algorithm for Mixed-Variable Optimization Problems

Many engineering design optimization problems can be represented as mixed-variable optimization problems. This study presents a heuristic approach for solving mixed-variable optimization using rotation and contraction of vectors. The current optimization algorithm, known as rotate-vector, has shown promising results in solving optimization problems with real or integer variables. Building upon this concept, the authors of this paper develop methods to solve 0-1 type and general discrete type variables, and introduce corresponding rotation and contraction operators. To address the simultaneous search and optimization of multiple variable types in nonlinear mixed integer programming problems, a grouping and comprehensive processing method is employed. The proposed group rotate-vector algorithm (GRV) is evaluated through the solution of multiple 0-1 programming problems and mixed-variable optimization problems. The study also investigates the impact of parameter settings on solution quality and efficiency. The experimental results demonstrate that the GRV algorithm outperforms other algorithms in terms of solution quality.