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Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. Recently, chaos theory has been successfully applied to solve these problems. In this paper, a novel hybrid solution based on SSA and chaos theory is proposed. The proposed Chaotic Salp Swarm Algorithm (CSSA) is applied on 14 unimodal and multimodal benchmark optimization problems and 20 benchmark datasets. Ten different chaotic maps are employed to enhance the convergence rate and resulting precision. Simulation results showed that the proposed CSSA is a promising algorithm. Also, the results reveal the capability of CSSA in finding an optimal feature subset, which maximizes the classification accuracy, while minimizing the number of selected features. Moreover, the results showed that logistic chaotic map is the optimal map of the used ten, which can significantly boost the performance of original SSA.

Selecting the most discriminative features is a challenging problem in many applications. Bio-inspired optimization algorithms have been widely applied to solve many optimization problems including the feature selection problem. In this paper, the most discriminating features were selected by a new Chaotic Dragonfly Algorithm (CDA) where chaotic maps embedded with searching iterations of the Dragonfly Algorithm (DA). Ten chaotic maps were employed to adjust the main parameters of dragonflies’ movements through the optimization process to accelerate the convergence rate and improve the efficiency of DA. The proposed algorithm is employed for selecting features from the dataset that were extracted from the Drug bank database, which contained 6712 drugs. In this paper, 553 drugs that were bio-transformed into liver are used. This data have four toxic effects, namely, irritant, mutagenic, reproductive, and tumorigenic effect, where each drug is represented by 31 chemical descriptors. The proposed model is mainly comprised of three phases; data pre-processing, features selection, and the classification phase. In the data pre-processing phase, Synthetic Minority Over-sampling Technique (SMOTE) was used to solve the problem of the imbalanced dataset. At the features selection phase, the most discriminating features were selected using CDA. Finally, the selected features from CDA were used to feed Support Vector Machine (SVM) classifier at the classification phase. Experimental results proved the capability of CDA to find the optimal feature subset, which maximizing the classification performance and minimizing the number of selected features compared with DA and the other meta-heuristic optimization algorithms. Moreover, the experiments showed that Gauss chaotic map was the appropriate map to significantly boost the performance of DA. Additionally, the high obtained value of accuracy (81.82–96.08%), recall (80.84–96.11%), precision (81.45–96.08%) and F-Score (81.14–96.1%) for all toxic effects proved the robustness of the proposed model.

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.

The whale optimization algorithm (WOA) is a novel evolutionary algorithm inspired by the behavior of whales. Similar to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real applications. This paper presents a novel chaotic whale optimization algorithm (CWOA) to overcome these problems where chaotic search is embedded in the searching iterations of WOA. Ten chaotic maps are considered to improve the performance of WOA. Experiments on ten benchmark datasets show the novel CWOA is effective for selecting relevant features with a high classification performance and a small number of features. Additionally the performance of CWOA is compared with WOA and ten other optimization algorithms. The experimental results show that circle chaotic map is the best chaotic map to significantly boost the performance of WOA. Moreover, chaotic with modifications of exploration operators outperform the highest performance.

In this paper, a new hybrid algorithm called quantum multiverse optimization (QMVO) is proposed. The proposed QMVO is based on quantum computing and multiverse optimization (MVO) algorithm. The main features of quantum theory and MVO were applied in a new algorithm to find the optimal trade-off between exploration and exploitation. QMVO algorithm depends on adopting a quantum representation of the search space and the integration of the quantum interference and operators in the multiverse optimization algorithm to obtain the optimal solution of the objective function. The performance of QMVO algorithm is evaluated by using 50 unimodal and multimodal benchmark functions. The experimental results show that the proposed algorithm has comprehensive superiority in solving complex numerical optimization problems. Also, the results show that the proposed QMVO is a promising optimization algorithm compared with other well-known and popular algorithms.

Liver cancer, which ranks sixth globally and third in cancer-related deaths, is caused by chronic liver disorders and a variety of risk factors. Despite therapeutic improvements, the prognosis for Hepatocellular Carcinoma (HCC) remains poor, with a 5-year survival rate for advanced cases of less than 12%. Although there is a noticeable decrease in the frequency of cases, liver cancer remains a significant worldwide health concern, with estimates surpassing one million cases by 2025. The prevalence of HCC has increased in Egypt, and it includes several neoplasms with distinctive messenger RNA (mRNA) and microRNA (miRNA) expression profiles. In HCC patients, certain miRNAs, such as miRNA-483-5P and miRNA-21, are upregulated, whereas miRNA-155 is elevated in HCV-infected people, encouraging hepatocyte proliferation. Short noncoding RNAs called miRNAs in circulation have the potential as HCC diagnostic and prognostic markers. This paper proposed a model for examining circulating miRNAs as diagnostic and predictive markers for HCC in Egyptian patients and their clinical and pathological characteristics. The proposed HCC detection model consists of three main phases: data preprocessing phase, feature selection based on the proposed Binary African Vulture Optimization Algorithm (BAVO) phase, and finally, classification as well as cross-validation phase. The first phase namely the data preprocessing phase tackle the main problems associated with the adopted datasets. In the feature selection based on the proposed BAVO algorithm phase, a new binary version of the BAVO swarm-based algorithm is introduced to select the relevant markers for HCC. Finally, in the last phase, namely the classification and cross-validation phase, the support vector machine and k-folds cross-validation method are utilized. The proposed model is evaluated on three studies on Egyptians who had HCC. A comparison between the proposed model and traditional statistical studies is reported to demonstrate the superiority of using the machine learning model for evaluating circulating miRNAs as diagnostic markers of HCC. The specificity and sensitivity for differentiation of HCC cases in comparison with the statistical-based method for the first study were 98% against 88% and 99% versus 92%, respectively. The second study revealed the sensitivity and specificity were 97.78% against 90% and 98.89% versus 92.5%, respectively. The third study reported 83.2% against 88.8% and 95.80% versus 92.4%, respectively. Additionally, the results show that circulating miRNA-483-5p, 21, and 155 may be potential new prognostic and early diagnostic biomarkers for HCC.

The red palm weevil (RPW) represents a significant danger to palm trees farms all over the world, which will result in considerable financial losses. The absence of apparent signs until the death of the palm tree makes it difficult to identify RPW infections at an early stage. The prompt detection of RPW diseases is further complicated by large-scale farms. In order to accomplish early detection of RPW using image analysis, this paper proposed a RPW classification model based on the proposed modified ResNet-34 deep learning architecture. A dataset of 483 images is used to assess the model’s performance. For the assessment, two different dataset settings are used. In the initial dataset setup, images are divided into three groups: adults, eggs, and Pupae. Four additional categories are added to the classification in the second dataset setup: female adults, male adults, eggs, and pupae. Experimental findings show the usefulness of the proposed model, with a remarkable total accuracy of 98% for both dataset setups. These results highlight the value of using the modified ResNet-34 architecture for the early detection of RPW. Moreover, the findings demonstrated that the proposed model offers great potential for decreasing the negative effects of RPW on palm tree farms and preventing financial losses in the agriculture sector.

Models based on machine learning algorithms have been developed to detect the breast cancer disease early. Feature selection is commonly applied to improve the performance of these models through selecting only relevant features. However, selecting relevant features in unsupervised learning is much difficult. This is due to the absence of class labels that guide the search for relevant information. This kind of the problem has rarely been studied in the literature. This paper presents a hybrid intelligence model that uses the cluster analysis algorithms with bio-inspired algorithms as feature selection for analyzing clinical breast cancer data. A binary version of both moth flame optimization and whale optimization algorithm is proposed. Two evaluation criteria are adopted to evaluate the proposed algorithms: clustering-based measurements and statistics-based measurements. The experimental results positively demonstrate that the capability of the proposed bio-inspired feature selection algorithms to produce both meaningful data partitions and significant feature subsets.

Meta-heuristic is defined as an iterative generation process with some random characters, which aims to generate a sufficiently good solution(s) for the global optimization problems. The process of solving a global optimization problem with near-optimal solutions is based on combining in some intelligent way different methodologies for exploiting and exploring the search space and building a structure information through different learning strategies. Optimal foraging algorithm (OFA) is a robust meta-heuristic algorithm inspired by following the animal foraging behavior. Recently, chaos and meta-heuristics have been combined in different algorithms with the aim of overcoming the limitations of meta-heuristics. In this paper, a novel algorithm for combining chaos with OFA is presented. The presented chaotic optimal foraging algorithm (COFA) is introduced with a set of unconstrained and constrained optimization problems using different chaotic maps. The results show that the proposed COFA outperforms the standard OFA for these benchmarks regarding exploitation, exploration, the trajectory of foraging individuals, search history, fitness improvement of the population and convergence rate. The performance of COFA has also compared with other most recent and popular meta-heuristic algorithms proofing its superior. An application of white blood cell segmentation in microscopic images has been chosen, and the proposed chaotic optimal foraging algorithm has been applied to see its ability and accuracy to identify and segment the white blood cell for further diagnosis. According to the statistical analysis of objective values, COFA algorithm is more accurate and robust than original OFA algorithm. COFA proves its ability to converge to the optimal multiple thresholds level-based segmentation more accurate than OFA. The experimental results also show that the proposed COFA proves its high degree of stability compared with original OFA in finding optimal multiple threshold values.

Air quality prediction is considered one of complex problems. This is due to volatility, dynamic nature, and high variability in space and time of particulates and pollutants. Meanwhile, designing an automated model for monitoring and predicting air quality becomes more and more relevant, particularly in urban regions. Air pollution can significantly affect the environment and eventually citizens’ health. In this paper, one of the popular machine learning algorithms, the neural network algorithm, is employed to classify different species of air pollutants. To boost the performance of the traditional neural network, the war strategy optimization algorithm tunes the neural network’s parameters. The experimental results demonstrate that the proposed optimized neural network based on the war strategy algorithm can accurately classify air pollutant species.