Differential evolution and threshold accepting hybrid algorithm for unconstrained optimisation
This paper develops a hybrid global optimisation metaheuristic methodology for solving unconstrained optimisation problems. The hybrid, to be called DETA, comprises two global optimisation algorithms viz. differential evolution (DE) and threshold accepting (TA) in tandem. While working with DE on benchmark problems, we noticed that it slows down before convergence is achieved. After analysing the possible reason for this shortcoming, we propose DETA to address it. DETA works in two phases: Phase 1 implements the original DE with relaxed convergence criterion. Then, a switch over is made from Phase 1 to Phase 2, where TA is used to quickly guide the search to global optimum. Performance of DETA is compared with that of DE on 26 unconstrained benchmark problems. The results obtained indicate that DETA hybrid is much superior to DE in terms of speed for the same level of accuracy.
differential evolution threshold accepting unconstrained optimisation global optimisation hybrid metaheuristics differential evolution-threshold accepting hybrid