A carnivorous plant algorithm for solving global optimization problems

In this study, a novel metaheuristic algorithm, namely, carnivorous plant algorithm (CPA), inspired by how the carnivorous plants adapting to survive in the harsh environment, was proposed. The CPA was first evaluated on thirty well-known benchmark functions with different characteristics and seven CEC 2017 test functions. Its convergence characteristic and computational time were analysed and compared with seven widely used metaheuristic algorithms, with the superiority was validated using the Wilcoxon signed-rank test. The applicability of the CPA was further examined on mechanical engineering design problems and a real-world challenging application of controlling the orientation of a five degree-of-freedom robotic arm. Experimental simulations demonstrated the supremacy of the CPA in solving global optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A novel metaheuristic algorithm inspired by carnivorous plants, the carnivorous plant algorithm (CPA), was proposed and validated for solving global optimization problems through experiments.