  This paper presents a comparative analysis of the performance of the
Incremental Ant Colony algorithm for continuous optimization
($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library.
The key objective is to understand how the various algorithms in the NLopt
library perform in combination with the Multi Trajectory Local Search (Mtsls1)
technique. A hybrid approach has been introduced in the local search strategy
by the use of a parameter which allows for probabilistic selection between
Mtsls1 and a NLopt algorithm. In case of stagnation, the algorithm switch is
made based on the algorithm being used in the previous iteration. The 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 benchmarks, we conclude that the best performing algorithm
is a hybrid variant of Mtsls1 with BFGS for local search.
