Mixed-discrete nonlinear optimization with simulated annealing

A global optimization algorithm for solving mixed-discrete nonlinear optimization problems is developed and presented. The algorithm makes use of a stochastic optimization technique-simulated annealing (SA). Approaches to handle constraints and various types of variables are discussed. The SA algorithm is found to be able to provide good, if not better, solutions when compared to existing mixed-discrete optimization algorithms based on the authors’ investigations and studies. The performance of the algorithm and comparisons between SA and those algorithms are demonstrated through 17 test problems.