Received February 5, 2022, accepted February 17, 2022, date of publication February 21, 2022, date of current version March 7, 2022. Digital Object Identifier 10.1109/ACCESS.2022.3153038 Grasshopper Optimization Algorithm With Crossover Operators for Feature Selection and Solving Engineering Problems AHMED A. EWEES 1 , (Senior Member, IEEE), MARWA A. GAHEEN 1 , ZAHER MUNDHER YASEEN 2,3,4 , AND RANIA M. GHONIEM 5,6 1 Department of Computer, Damietta University, Damietta 34511, Egypt 2 Adjunct Research Fellow, USQ’s Advanced Data Analytics Research Group, School of Mathematics Physics and Computing, University of Southern Queensland, Darling Heights, QLD 4350, Australia 3 New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar 64001, Iraq 4 Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA, Shah Alam, Selangor 40450, Malaysia 5 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia 6 Department of Computer, Mansoura University, Mansoura 35516, Egypt Corresponding author: Ahmed A. Ewees (ewees@du.edu.eg) ABSTRACT Feature selection (FS) is an irreplaceable phase that makes data mining more efficient. It effectively enhances the implementation and decreases the computational problem of learning models. The comprehensive and greedy algorithms are not suitable for the present growing number of features when detecting the optimal subset. Thus, swarm intelligence algorithms (SI) are becoming more common in dealing with FS problems. The grasshopper optimizer algorithm (GOA) represents a new SI; it showed good performance in different fields. Another promising nature-inspired algorithm is a salp swarm algorithm, denoted as SSA, an SI used to tackle optimization issues. In this paper, two phases are applied to propose a new method using crossover-salp swarm with grasshopper optimization algorithm (cSG). In this method, the crossover operators are used to maintain the population of the SSA then the improved SSA is used as a local search to boost the exploration phase of the GOA. Subsequently, this improvement prevents the cSG from premature convergence, high computation time, and being trapped in local minimum. To confirm the effectiveness of proposed cSG method, it is evaluated in different optimizations problems. Eventually, the obtained results are compared to a number of well-known algorithms over global optimization, feature selection datasets, and six real-engineering problems. Experimental results point out that the cSG is superior in solving different optimization problems due to the integration of crossover operators and SSA which enhances its performance and flexibility. INDEX TERMS Grasshopper optimization algorithm, crossover operator, salp swarm algorithm, optimiza- tion problems, feature selection, engineering problems. I. INTRODUCTION Recently, feature selection (FS) has acquired much attention from researchers working in machine learning and data min- ing domain. However, the increase in data size and dimen- sions causes different problems, such as the appearance of noisy, inconvenient and redundant data. Hence, it is hard to find optimal group of features and remove redundant ones. Dealing with datasets, some features are insignificant in the The associate editor coordinating the review of this manuscript and approving it for publication was Li He . presence of irrelevancy and redundancy. Therefore, consider- ing such features is not valuable and usually affects the clas- sification accuracy [1]. FS attempts to enhance classification performance through selecting from the original enormous range of features just a small subset of suitable features [2]. The extraction of redundant and irrelevant features will, thus, minimize the data dimensionality, enhance the learning process by simplifying model learning and enhancing per- formance [3], [4]. Other benefits of FS are that: reducing overfitting minimizes redundant data, decreases chances for noise-based rulemaking, enhances precision and minimizes 23304 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 10, 2022