A water cycle algorithm based on quadratic interpolation for high-dimensional global optimization problems

The water cycle algorithm (WCA) is easily trapped in local optimal solutions when dealing with high-dimensional optimization problems and has low precision and slow convergence. A WCA based on quadratic interpolation (QIWCA) is proposed in this study to address these drawbacks. First, a new nonlinear adjustment strategy for distance control parameters is designed to balance the exploration and exploitation capabilities of the algorithm. Second, during the search process of the algorithm, mutation operations are probabilistically performed to enhance the global exploration capability of the algorithm. Lastly, the quadratic interpolation operator is introduced to improve the local exploitation capability of the algorithm. QIWCA is also compared with several of the most advanced meta-heuristic algorithms on 46 benchmark functions. Experimental results show that QIWCA outperforms the compared algorithms in terms of convergence speed, global exploration capability, solution accuracy, and reliability.