A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization

• Published in: IEEE Access
• Main Authors: Hongping Hu, Linmei Zhang, Yanping Bai, Peng Wang, Xiuhui Tan
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2019
• Subjects: Squirrel search algorithm; invasive weed optimization; support vector machine; deterministic maximum-likelihood; optimization; Electrical engineering. Electronics. Nuclear engineering; TK1-9971; DML
• Online Access: https://doi.org/10.1109/ACCESS.2019.2932198; https://doaj.org/article/600e70cf50c3455ba9f79ad095bd1426

This paper proposes a hybrid algorithm called ISSA based on the combination of squirrel search algorithm (SSA) proposed in 2019 and invasive weed optimization (IWO) proposed in 2006. About 36 benchmark functions are employed to test the performances of ISSA. Then, ISSA is combined with support vector machine (SVM) and deterministic maximum-likelihood (DML) algorithm, respectively, and the two corresponding models ISSA-SVM and ISSA-DML are established for performing the grade classifications of air quality and the direction of arrival (DOA) estimation of MEMS vector hydrophone, respectively. The results of 36 benchmark functions prove that the proposed ISSA is able to provide very competitive results in terms of the average values, the standard derivation, and the convergence curves. The average accuracy rate of classification of ISSA-SVM model is the best and reaches 87.91971%, and the DOA estimations of ISSA-DML have the least root mean square error (RMSE) and the closest to the actual angles. Therefore, it is concluded that the proposed ISSA is an effective algorithm for function optimizations and is suitable to be combined with other algorithms and machine learning for classification and estimation.