Applying autonomous hybrid agent-based computing to difficult optimization problems

Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relevant Markov-Chain model. Now research continues introducing autonomous hybridization into EMAS. This paper focuses a proposed hybrid version EMAS, and covers selection introduction number operators defining rules for starting steps main algorithm. Those leverage existing, well-known to be efficient metaheuristics, integrate results The discussed modifications evaluated difficult continuous-optimization benchmarks.