Enhanced Water Cycle Algorithm with Active Learning and Return Strategy

Abstract

In order to improve the performance of Water Cycle Algorithm (WCA), an alternative adaptation approach for enhancing the global searching ability is proposed. The proposed algorithm, named WCA-ALR, uses a new diversity enhancement approach to effectively improve the exploration capability of the WCA. The proposed approach consists of two major modifications: (1) an active selection method for choosing learning targets; (2) a promising position sifting and returning strategy. The benefits prove that actively selecting a learning target performs better than that of learning from a fixed one. A promising position sifting and returning strategy can also enhance the exploration ability. In order to verify the performance, numerical experiments on five basic benchmark problems are conducted. Then, a set of benchmark problems from the CEC2017 on 10 and 30 dimensions are used to prove the effectiveness of WCA-ALR. Experimental results affirm that the proposed approach can obtain better results, compared to the original WCA.