A new indicator-based many-objective ant colony optimizer for continuous search spaces

In this paper, we propose a novel multi-objective ant colony optimizer (called iMOACO$$_{\mathbb {R}}$$R) for continuous search spaces, which is based on ACO$$_{\mathbb {R}}$$R and the R2 performance indicator. iMOACO$$_{\mathbb {R}}$$R is the first multi-objective ant colony optimizer (MOACO) specifically designed to tackle continuous many-objective optimization problems (i.e., multi-objective optimization problems having four or more objectives). Our proposed iMOACO$$_{\mathbb {R}}$$R is compared to three state-of-the-art multi-objective evolutionary algorithms (NSGA-III, MOEA/D and SMS-EMOA) and a MOACO algorithm called MOACO$$_{\mathbb {R}}$$R using standard test problems and performance indicators taken from the specialized literature. Our experimental results indicate that iMOACO$$_{\mathbb {R}}$$R is very competitive with respect to NSGA-III and MOEA/D and it is able to outperform SMS-EMOA and MOACO$$_{\mathbb {R}}$$R in most of the test problems adopted.