A Hybrid Genetic Algorithm for Mixed-Discrete Design Optimization

Abstract

A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with some other GAs or random search methods. Several practical examples of mechanical design are tested using the computer program developed. The numerical results demonstrate the effectiveness and robustness of the proposed approach.