Enhancing IACO R Local Search by Mtsls1-BFGS for Continuous Global Optimization
U. K. Arun Kumar, Jayadeva, Sumit Soman · 2015
A widely known approach for continuous global optimization has been the Incremental Ant Colony Framework (IACOR). In this paper, we propose a strategy to introduce hybridization within the exploitation phase of the IACOR framework by using the Multi-Trajectory Local Search (Mtsls1) algorithm and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms. Our approach entails making a probabilistic choice between these algorithms. In case of stagnation, we switch the algorithm being used based on the last iteration. We evaluate our approach on the Soft Computing (SOCO) benchmark functions and present results by computing the mean and median errors on the global optima achieved, as well as the iterations required. We compare our approach with competing methods on a number of benchmark functions, and show that the proposed approach achieves improved results. In particular, we obtain the global optima in terms of average value for 14 out of 19 benchmark functions, and in terms of the median value for all SOCO benchmarks. At the same time, the proposed approach uses fewer function evaluations on several benchmarks when compared with competing methods, which have been found to use 54% more function evaluations.