Benchmarking NLopt and state-of-the-art algorithms for continuous global optimization via IACORIACOR

This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization (IACORIACOR), with different algorithms provided in the NLopt library. The key objective is to understand how various algorithms in the NLopt library perform in combination with the Multi-Trajectory Local Search (Mtsls1) technique. A hybrid approach has been introduced for the local search strategy, by the use of a parameter that allows for probabilistic selection between Mtsls1 and the NLopt algorithm. In case of stagnation, a switch is made based on the algorithm being used in the previous iteration. This paper presents an exhaustive comparison on the performance of these approaches on Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014 benchmarks. For both sets of benchmarks, we conclude that the best performing algorithm is a hybrid variant of Mtsls1 with BFGS for local search.