Abstract
This paper proposes a new method which is called as Adaptive Range Differential Evolution (ARDE), based on Adaptive Range Particle Swarm Optimization (ARPSO). In this technique an active search domain range is determined by utilizing the mean and standard deviation of each design variable. In the initial search stage, the search domain is explored widely. Then the search domain is shrunk so that it is restricted to a small domain while the search continues. To achieve these search processes, the expanded domain is proposed. The search domain is constructed by utilizing the mean, standard deviation and the best position in all particles. Through these processes, it is possible to shrink the active search domain range. Moreover, by using the proposed method, an optimum solution is attained with high accuracy and a small number of function evaluations. Through numerical examples, the effectiveness and validity of ARDE are examined.
