Socio-cognitively inspired ant colony optimization

- The paper presents a concept of socio-cognitive ACO.
- The referenced works clearly show relation to the existing state of the art and the previously described papers on this topic by the authors.
- Besides the newly proposed metaheuristic algorithm the paper focuses on its evaluations on different TSP problems from TSPLIB library.

Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive features of a population, incorporating perspective-taking ability to generate differently acting ant colonies. Although our main goal was simulation, we took advantage of the fact that the quality of the constructed system was evaluated based on selected traveling salesman problem instances, and the resulting computing system became a metaheuristic, which turned out to be a promising method for solving discrete problems. In this paper, we extend the initial sets of populations driven by different perspective-taking inspirations, seeking both optimal configuration for solving a number of TSP benchmarks, at the same time constituting a tool for analyzing socio-cognitive features of the individuals involved. The proposed algorithms are compared against classic ACO, and are found to prevail in most of the benchmark functions tested.