An Evolutionary Dynamic Control Cuckoo Search Algorithm for Solving the Constrained Engineering Design Problems

The key contribution of this work is the dynamic control cuckoo search (DCCS) method. Nonetheless, the adaptive cuckoo search (ACS) appears to be effective in utilizing the exploitation and exploration by using the best solution followed by an adaptive step size to determine the next-generation solutions. However, its convergence rate is limited. To solve this problem, the authors use dynamic control, adaptive step size, and randomization in the cuckoo search path for the following generations. A better tradeoff between exploitation and exploration is achieved, allowing for a faster convergence rate. The 23 traditional and 10 CEC2019 benchmark functions are used for validations. When the DCCS results are compared to the well-known methods using scalability and statistical tests like Wilcoxon's rank-sum test, it shows a significant improvement. Friedman's mean rank test is also ranked the strategic DCCS top. Furthermore, constrained engineering design problems 1) welded beam design and 2) pressure vessel design are solved. The DCCS would be useful for optimization.