Frequency-constrained truss optimization using the fruit fly optimization algorithm with an adaptive vision search strategy

This study proposes a memory-based search strategy for the fruit fly optimization algorithm (FOA) to enrich its search ability, and utilizes an improved Deb (IDeb) rule to tackle the constraints and increase the computational efficiency of the FOAs. Unlike other FOAs that employ a predefined search radius, the proposed search strategy mines the knowledge of both the swarm and the individuals to adaptively determine the vision search radius of each fruit fly. The improved Deb rule predicated on the elitism of the FOAs can eliminate redundant structural analyses in the optimization process without compromising the quality of the identified optimal solution. Four frequency-constrained truss optimization problems are presented to examine the efficiency of the proposed approach. The results demonstrate that the FOA that hybridizes both the traditional and the proposed search strategy exhibits the best performance, and the IDeb rule substantially increase the computational efficiency of FOAs in structural optimization.