diferenciální evoluce, adaptace, globální optimalizace, optimalizační soutěže

Differential Evolution (DE) is an efficient tool for global optimization. Despite this efficiency, there are issues as the stagnation and premature convergence which have to be solved. Recently we published several papers. - In the first one, we validated the efficiency of our combined approaches on Real-World problems. - The second paper revisits the performance of the exponential crossover on Real-World problems. - In the next one, we proposed new DE variant where mutant vector is selected based on Euclidean distance. - Further we verify if exponential crossover improves the power of successful L-SHADE variant. - In the last discussed paper, we proposed new adaptive mechanism for population size based on population diversity. These results and our recent still unpresented ones are promised but there are many unsolved problems so we are working on it continuously.