Cuckoo search algorithm for applied structural and design optimization: Float system for experimental setups

In the present study, Cuckoo Search (CS) as a nature-inspired optimization algorithm was applied for structural and design optimization of a new float system for experimental setups. For this purpose, based on the setup configuration, it was tried to minimize the total length of the float, while maintaining the structural and performance-based constraints. Different geometries for the float structure were examined to come up with the feasible options. The problem was formulated into a constrained optimization in terms of four or five variables, depending on the geometry, along with two performance-based constraints and some structural constraints. CS was used to solve the constrained optimization problem and the convergence trends of the parameters to optimal solutions were examined in details. Generalized reduced gradient (GRG) method known as GRG nonlinear was also used for validation and comparison purpose. The results of the optimization and the performance of the float produced showed that CS can be used as a powerful tool for applied structural and design problems. It should be mentioned that the float problem can be used as a benchmark structural design problem for validation of new optimization algorithms. Besides, the optimal float can be produced for various experimental setups with different structures and constraints, depending on the application. Highlights Cuckoo Search (CS) algorithm as a metaheuristic approach. Constrained optimization in structural design using CS algorithm. Designing a new float for experimental setups. Production of an optimal float for measurement system. Float design as a benchmark problem for optimization algorithms.