Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities

Houssein, Essam H.; Neggaz, Nabil; Hosney, Mosa E.; Mohamed, Waleed M.; Hassaballah, M.

doi:10.1007/s00521-021-05991-y

Cited by 51 publications

(11 citation statements)

References 53 publications

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“…To make the experimental findings more representative, the ERHHO is compared with algorithms of SMA [ 27 ], WOA [ 24 ], SSA [ 25 ], SCA [ 26 ], and HHO [ 36 ], DHHO/M [ 43 ], and HHOCM [ 41 ]. Table 2 shows the parameter settings for each algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Ma et al [ 40 ] used the Chan algorithm to calculate the initial solution and replace an individual position to reduce unnecessary exploration and improve the algorithm's convergence speed. Houssein et al [ 41 ] introduced cross and mutation cooperative gene operators and proposed the HHOCM optimization algorithm based on opposition-based learning, which enhanced the ability of exploration and applied it to generate the initial population effectively. Tang et al [ 42 ] introduced the tent chaotic map, an elite hierarchy system, nonlinear escape energy strategy, and Gaussian random walk strategy to improve the convergence speed and accuracy of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Song

Jia

Abualigah

et al. 2022

Computational Intelligence and Neuroscience

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One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Song

Jia

Abualigah

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…Houssien et al [278] used genetic algorithm operators (crossover and mutation) in addition to OBL and random OBL strategies in classical HHO to select chemical descriptors/features and compound activities.…”

Section: Chemical Engineering and Drug Discoverymentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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“…Because meta-heuristic algorithms have great performance and are straightforward to implement, researchers have widely proved their experience to handle many types of challenging optimization issues in engineering, communications, industry, and social sciences [ 20 ]. In addition, they have been employed in biological information [ 21 ], chemical information [ 22 ], feature selection [ 23 ], task scheduling in cloud computing [ 24 ], image segmentation [ 25 ], global optimization [ 26 ], and as well as cost-effective emission dispatch [ 27 ]. There are various meta-heuristic algorithms, of which the marine predators algorithm (MPA) [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

Houssein

Emam

Ali

2022

Neural Comput & Applic

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Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.

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Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities

Cited by 51 publications

References 53 publications

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

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