Generalized swarm intelligence algorithms with domain-specific heuristics

P. Matrenin (Novosibirsk State Technical University)
V. Myasnichenko (Tver State University)
N. Sdobnyakov (Tver State University)
D. Sokolov (Tver State University)
S. Fidanova (Bulgarian Academy of Sciences, Institute of Information and Communication Technologies)
L. Kirilov (Bulgarian Academy of Sciences, Institute of Information and Communication Technologies)
R. Mikhov (Bulgarian Academy of Sciences, Institute of Information and Communication Technologies)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 10, No 1: March 2021

In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.