A Novel TRUST-TECH Guided Branch-and-Bound Method for Nonlinear Integer Programming (original) (raw)

2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems

Nonlinear integer programming has not reached the same level of maturity as linear programming, and is still difficult to solve, especially for large-scale systems. Branch-and-Bound and its variants are widely-used methods for integer programming, and numerical solutions obtained by them still can be far away from the global optimum. In this paper, we propose a novel approach to guide the deterministic/heuristic methods and the commercial solvers for nonlinear integer programming, and aim at improving the solution quality by taking advantage of Transformation Under Stability-reTraining Equilibrium Characterization (TRUST-TECH) method. Moreover, we examine the effectiveness by developing and simulating TRUST-TECH guided Branch-and-Bound and TRUST-TECH guided commercial solver(s), and compare their performance with that of the original methods/solvers (e.g. GAMS/BARON, GAMS/SCIP, LINDO/MINLP) and also with that of recently-reported Evolutionary-Algorithm based methods. Simulation results provide evidence that, the solution quality is substantially improved, and the global-optimal solutions are usually obtained after the application of TRUST-TECH. The proposed approach can be immediately utilized to guide other Evolutionary-Algorithm based methods and commercial solvers which incorporate intelligent searching components.