Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

Abstract

Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning. INDEX TERMS Di-2-Ethylhexyl phthalate; Hepatotoxicity; Support vector machine; Harris hawks optimization; Salp swarm algorithm; Grey wolf optimizer.

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

“…16,19,20,[46][47][48] Among metaheuristic methods, there are many options, and here we review a few of their applied cases including differential evolution (DE), 49 whale optimizer algorithm (WOA), [50][51][52] slime mould algorithm (SMA) * , 53 Runge-Kutta optimizer (RUN), 54 hunger games search (HGS), 55 colony predation algorithm (CPA), 56 and Harris hawks optimizer (HHO) † . [57][58][59][60][61][62] The grey wolf optimizer (GWO) 63 proposed in 2014 based on the metaphor of wolf leadership and communication for their prey. A set of metaheuristic algorithms have the specific parameters listed in Appendix A.…”

“…Metaheuristic methods are generally used to work out these global optimization problems because of their advantages such as simplicity, high quality, and low computational cost 16,19,20,46–48 . Among metaheuristic methods, there are many options, and here we review a few of their applied cases including differential evolution (DE), 49 whale optimizer algorithm (WOA), 50–52 slime mould algorithm (SMA) * , 53 Runge‐Kutta optimizer (RUN), 54 hunger games search (HGS), 55 colony predation algorithm (CPA), 56 and Harris hawks optimizer (HHO) † 57–62 63 proposed in 2014 based on the metaphor of wolf leadership and communication for their prey.…”

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

“…Survival exploration strategies applied successfully to the structure of the HHO, which resulted in efficient results compared to other competitors [ 65 ]. Authors developed a Gaussian bare bone HHO in [ 66 ] for predicting entrepreneurial intentions.…”

“…A multi-population DE-based version was also proposed that can show excellent exploratory patterns [ 67 ]. HHO and its progressive variants also applied to parameters identification of photovoltaic cells [ 60 , 68 ], image segmentation [ 69 , 70 ], web service composition [ 71 ], diagnosing coronavirus disease [ 72 ], predicting di-2-ethylhexyl phthalate toxicity [ 65 ], parameter estimation of photovoltaic models [ 73 , 74 ], real-world engineering optimization problem [ 75 ], and feature selection [ 76 , 77 ]. For a review of recent works on HHO, please refer to work in [ 78 ].…”

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

“…Most of these methods work based on switching the exploration and exploitation phases using stochastic operations [62,72]. Most researchers try to boost the efficacy based on balancing the initial cores of these methods [59,[73][74][75][76][77][78][79][80][81][82]. The recent efficient variants of swarm intelligence optimization algorithms are simulated annealing algorithm (SA) [83,84], fruit fly optimization algorithm (FOA) [85,86], sine cosine algorithm (SCA) [71,[87][88][89], moth-flame optimization (MFO) [90,91], particle swarm optimization (PSO) [92], whale optimizer (WOA) [93], different evolution (DE) [94], bat-inspired algorithm (BA) [95], grey wolf optimization (GWO) [96][97][98][99][100][101], grasshopper optimization algorithm (GOA) [102], Harris hawks optimization (HHO) (https://aliasgharheidari.…”