Optimization using fuzzy macroevolutionary algorithm and uniform design techniques

Macroevolutionary algorithm (MA) is a new approach to optimization problems based on extinction patterns in macroevolution. In MAs, evolves at the level of higher taxa is used as the underlying metaphor. It is inspired by the latest models about evolution at large scale-macroevolution, while the traditional evolutionary algorithms are inspired by the natural selection of Darwinian theory. The MA model exploits the presence of links between "species" that represent candidate solutions to the optimization problem. In this paper, a new version of MA called fuzzy MA is proposed to solve complicated multi-modal optimization problems. It makes full use of the advantages of fuzzy representation in problem solving. In addition, uniform design technique is used to improve the search speed and solution quality. Numerical simulation results show the power of this new algorithm.