Abstract :
Differential evolution (DE) is a simple evolutionary algorithm for numerical optimization whose most novel feature is that it mutates vectors by adding weighted, random vector differentials to them. A new version of the DE algorithm is described and the results of its attempts to optimize the 7 real-valued functions of the 2nd ICEO are tabulated. DE succeeded in finding each function´s global minimum, although the number of evaluations needed in one instance was unacceptably high. Despite this lone difficulty, DE´s speed of execution across the remaining test bed, in addition to its simplicity, robustness and ease of use, suggest that it is a valuable tool for continuous numerical optimization
Keywords :
differential equations; genetic algorithms; simulated annealing; vectors; continuous numerical optimization; differential evolution; evaluations; evolutionary algorithm; execution speed; function global minimum; real-valued functions; test bed; vector mutation; weighted random vector differentials; Algorithm design and analysis; Annealing; Costs; Evolutionary computation; Genetic mutations; History; Neodymium; Polynomials; Robustness; Testing;
