Grasshopper Optimization Algorithm With Crossover Operators for Feature Selection and Solving Engineering Problems

Zaher Mundher Yaseen,Ahmed A. Ewees,Marwa A. Gaheen,Rania M. Ghoniem

doi:10.1109/access.2022.3153038

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.

Highlights

Feature selection (FS) has acquired much attention from researchers working in machine learning and data mining domain
The problem of tension/compression spring design problem was extensively addressed through various bio-inspired optimization algorithms including: multi-verse optimizer (MVO), GSA, particle swarm optimizer (PSO), WOA, GWO, MFO, salp swarm algorithm (SSA) and RO
In this work, an improved grasshopper optimization algorithm (GOA) is proposed by applying the crossover operators to maintain the population of the salp swarm algorithm (SSA), the improved SSA is applied as local search to the original GOA

Summary

Introduction

Feature selection (FS) has acquired much attention from researchers working in machine learning and data mining domain. Some features are insignificant in the presence of irrelevancy and redundancy. Considering such features is not valuable and usually affects the classification 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, minimize the data dimensionality, enhance the learning process by simplifying model learning and enhancing performance [3], [4]. Other benefits of FS are that: reducing overfitting minimizes redundant data, decreases chances for noise-based rulemaking, enhances precision and minimizes misleading data which means enhancing the precision of modeling. Decreases training time, minimizes data points, reduces complexity of the algorithms, and accelerates the algorithm's training [5], [6]

Methods

Results

Conclusion