Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures

The performance of differential evolution (DE) mostly depends on mutation operator. Inappropriate configurations of mutation strategies and control parameters can cause stagnation due to over exploration or premature convergence due to over exploitation. Balancing exploration and exploitation is crucial for an effective DE algorithm. This work presents an enhanced DE (EDE) for truss design that utilizes two new strategies, namely,integrated mutationandadaptive mutation factorstrategies, to obtain a good balance between the exploration and exploitation of DE. Three mutation strategies (DE/rand/1,DE/best/2, andDE/rand-to-best/1) are combined in theintegrated mutationstrategy to increase the diversity of random search and avoid premature convergence to a local minimum. Theadaptive mutation factorstrategy systematically adapts the mutation factor from a large value to a small value to avoid premature convergence in the early searching period and to increase convergence to the global optimum solution in the later searching period. The outstanding performance of the proposed EDE is demonstrated through optimization of five truss structures.

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.