Generalized swarm intelligence algorithms with domain-specific heuristics

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 domainspecific 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.