Orthogonal learning covariance matrix for defect of grey wolf optimizer: Insights, balance, diversity, and feature selection

08/16/2021
by   Ali Asghar Heidari, et al.
0

This research’s genesis is in two aspects: first, a guaranteed solution for mitigating the grey wolf optimizer’s (GWO) defect and deficiencies. Second, we provide new open-minding insights and deep views about metaheuristic algorithms. The population-based GWO has been recognized as a popular option for realizing optimal solutions. Despite the popularity, the GWO has structural defects and uncertain performance and has certain limitations when dealing with complex problems such as multimodality and hybrid functions. This paper tries to overhaul the shortcomings of the original process and develops a GWO variant enhanced with a covariance matrix adaptation evolution strategy (CMAES), levy flight mechanism, and orthogonal learning (OL) strategy named GWOCMALOL. The algorithm uses the levy flight mechanism, orthogonal learning strategy, and CMAES to bring more effective exploratory inclinations. We conduct numerical experiments based on various functions in IEEE CEC2014. It is also compared with 10 other algorithms with competitive performances, 7 improved GWO variants, and 11 advanced algorithms. Moreover, for more systematic data analysis, Wilcoxon signed-rank test is used to evaluate the results further. Experimental results show that the GWOCMALOL algorithm is superior to other algorithms in terms of convergence speed and accuracy. The proposed GWO-based version is discretized into a binary tool through the transformation function. We evaluate the performance of the new feature selection method based on 24 UCI data sets. Experimental results show that the developed algorithm performs better than the original technique, and the defects are resolved. Besides, we could reach higher classification accuracy and fewer feature selections than other optimization algorithms. A narrative web service at http://aliasgharheidari.com will offer the required data and material about this work.

READ FULL TEXT

page 2

page 11

page 12

page 15

page 21

page 22

page 27

page 32

research
11/20/2020

Multi-core sine cosine optimization: Methods and inclusive analysis

A public repository will support this research at http://aliasgharheidar...
research
11/20/2020

Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

The first powerful variant of the Harris hawks optimization (HHO) is pro...
research
12/19/2022

Performance assessment and exhaustive listing of 500+ nature inspired metaheuristic algorithms

Metaheuristics are popularly used in various fields, and they have attra...
research
11/20/2020

Orthogonal Learning Harmonizing Mutation-based Fruit Fly-inspired Optimizers

The original fruit fly optimizer (FOA) has two core disadvantages: slow ...
research
11/01/2020

Ant Colony Optimization with Horizontal and Vertical Crossover Search: Fundamental Visions for Multi-threshold Image Segmentation

The ant colony optimization (ACO) is the most exceptionally fundamental ...
research
08/16/2021

Evolving Fuzzy k-Nearest Neighbors Using an Enhanced Sine Cosine Algorithm: Case Study of Lupus Nephritis

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (...

Please sign up or login with your details

Forgot password? Click here to reset