An improved RIME optimization algorithm for lung cancer image segmentation

Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.

Optimizing prediction accuracy for early recurrent lumbar disc herniation with a directional mutation-guided SVM model

Percutaneous endoscopic lumbar discectomy (PELD) is one of the main means of minimally invasive spinal surgery, and is an effective means of treating lumbar disc herniation, but its early recurrence is still difficult to predict. With the development of machine learning technology, the auxiliary model based on the prediction of early recurrent lumbar disc herniation (rLDH) and the identification of causative risk factors have become urgent problems in current research. However, the screening ability of current models for key factors affecting the prediction of rLDH, as well as their predictive ability, needs to be improved. Therefore, this paper presents a classification model that utilizes wrapper feature selection, developed through the integration of an enhanced bat algorithm (BDGBA) and support vector machine (SVM). Among them, BDGBA increases the population diversity and improves the population quality by introducing directional mutation strategy and guidance-based strategy, which in turn allows the model to secure better subsets of features. Furthermore, SVM serves as the classifier for the wrapper feature selection method, with its classification prediction results acting as a fitness function for the feature subset. In the proposed prediction method, BDGBA is used as an optimizer for feature subset filtering and as an objective function for feature subset evaluation based on the classification results of the support vector machine, which improves the interpretability and prediction accuracy of the model. In order to verify the performance of the proposed method, this paper proves the performance of the model through global optimization experiments and prediction experiments on real data sets. The accuracy of the proposed rLDH prediction model is 93.49% and sensitivity is 88.33%. The experimental results show that Level of herniated disk, Modic change, Disk height, Disk length, and Disk width are the key factors for predicting rLDH, and the proposed method is an effective auxiliary diagnosis method.

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

This study predicts the parameters such as viscosity and thermal conductivity in silica-alumina-MWCN/water nanofluid using the artificial intelligence method and using design variables such as solid volume fraction and temperature. In this study, 6 optimization algorithms were used to predict and numerically model the μnf and TC of silica-alumina-MWCNT/water-NF. In this study, six measurement criteria were used to evaluate the estimates obtained from the coupling process of GMDH ANN with each of these 6 optimization algorithms. The results reveal that the influence of the φ is notably higher on both μnf and TC with values of 0.83 for μnf and 0.92 for TC, while Temp has a relatively weaker impact with -0.5 for μnf and 0.38 for TC. Among various algorithms, the coupling of the evolutionary algorithm NSGA II with ANN and GMDH performs best in predicting μnf and TC for the NF, with a maximum margin of deviation of -0.108 and an R2 evaluation criterion of 0.99996 for μnf and 1 for TC, indicating exceptional model accuracy. In the subsequent phase, a meta-heuristic Genetic Algorithm minimizes μnf and TC values. Four points (A, B, C, and D) along the Pareto front are selected, with point A representing the optimal state characterized by low values of φ and Temp (0.0002 and 50.8772, respectively) and corresponding target function values of 0.9988 for μnf and 0.6344 for TC. In contrast, point D represents the highest values of φ and Temp (0.49986 and 59.9775, respectively) and yields target function values of 2.382 for μnf and 0.8517 for TC. This analysis aids in identifying the optimal operating conditions for maximizing NF performance.

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.

Design optimization of office building envelope by developed farmland fertility algorithm for energy saving

This study focuses on designing sustainable buildings with a specific emphasis on reducing energy consumption and optimizing costs. To address the time-consuming nature of simulation software like TRNSYS and Energy Plus, a novel meta-heuristic optimization algorithm called the Developed Optimization Algorithm of Farmland Fertility (DFFA) is provided. The DFFA algorithm is utilized to optimize parameters related to the building envelope, encompassing walls, windows, and glass curtain walls, aiming to minimize energy demand and construction expenses. Comparative analysis with other approaches such as EPO, LOA, MVO, and FFA demonstrates significant reductions in energy consumption and building design costs achieved by employing the proposed algorithm. Furthermore, the DFFA algorithm yields the desired results within fewer iterations. By increasing the surface area of the glass curtain wall and total window space, improvements in natural ventilation and interior lighting are observed. Despite similar window opening measurements across the compared methods, the suggested algorithm surpasses others when it comes to overall cost and energy efficiency. The total cost reduction compared to the initial design amounts to 39 %. Thus, the DFFA algorithm proves to be more effective in conserving energy in buildings compared to other analyzed procedures. This research serves as a case study and presents a promising method applicable to designing various building types under different weather conditions in future projects.

An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.

Red piranha optimization (RPO): a natural inspired meta-heuristic algorithm for solving complex optimization problems

An optimization algorithm is a step-by-step procedure which aims to achieve an optimum value (maximum or minimum) of an objective function. Several natural inspired meta-heuristic algorithms have been inspired to solve complex optimization problems by utilizing the potential advantages of swarm intelligence. In this paper, a new nature-inspired optimization algorithm which mimics the social hunting behavior of Red Piranha is developed, which is called Red Piranha Optimization (RPO). Although the piranha fish is famous for its extreme ferocity and thirst for blood, it sets the best examples of cooperation and organized teamwork, especially in the case of hunting or saving their eggs. The proposed RPO is established through three sequential phases, namely; (i) searching for a prey, (ii) encircling the prey, and (iii) attacking the prey. A mathematical model is provided for each phase of the proposed algorithm. RPO has salient properties such as; (i) it is very simple and easy to implement, (ii) it has a perfect ability to bypass local optima, and (iii) it can be employed for solving complex optimization problems covering different disciplines. To ensure the efficiency of the proposed RPO, it has been applied in feature selection, which is one of the important steps in solving the classification problem. Hence, recent bio-inspired optimization algorithms as well as the proposed RPO have been employed for selecting the most important features for diagnosing Covid-19. Experimental results have proven the effectiveness of the proposed RPO as it outperforms the recent bio-inspired optimization techniques according to accuracy, execution time, micro average precision, micro average recall, macro average precision, macro average recall, and f-measure calculations.

Binary Simulated Normal Distribution Optimizer for feature selection: Theory and application in COVID-19 datasets

Classification accuracy achieved by a machine learning technique depends on the feature set used in the learning process. However, it is often found that all the features extracted by some means for a particular task do not contribute to the classification process. Feature selection (FS) is an imperative and challenging pre-processing technique that helps to discard the unnecessary and irrelevant features while reducing the computational time and space requirement and increasing the classification accuracy. Generalized Normal Distribution Optimizer (GNDO), a recently proposed meta-heuristic algorithm, can be used to solve any optimization problem. In this paper, a hybrid version of GNDO with Simulated Annealing (SA) called Binary Simulated Normal Distribution Optimizer (BSNDO) is proposed which uses SA as a local search to achieve higher classification accuracy. The proposed method is evaluated on 18 well-known UCI datasets and compared with its predecessor as well as some popular FS methods. Moreover, this method is tested on high dimensional microarray datasets to prove its worth in real-life datasets. On top of that, it is also applied to a COVID-19 dataset for classification purposes. The obtained results prove the usefulness of BSNDO as a FS method. The source code of this work is publicly available at https://github.com/ahmed-shameem/Feature_selection.

BCOVIDOA: A Novel Binary Coronavirus Disease Optimization Algorithm for Feature Selection

The increased use of digital tools such as smart phones, Internet of Things devices, cameras, and microphones, has led to the produuction of big data. Large data dimensionality, redundancy, and irrelevance are inherent challenging problems when it comes to big data. Feature selection is a necessary process to select the optimal subset of features when addressing such problems. In this paper, the authors propose a novel Binary Coronavirus Disease Optimization Algorithm (BCOVIDOA) for feature selection, where the Coronavirus Disease Optimization Algorithm (COVIDOA) is a new optimization technique that mimics the replication mechanism used by Coronavirus when hijacking human cells. The performance of the proposed algorithm is evaluated using twenty-six standard benchmark datasets from UCI Repository. The results are compared with nine recent wrapper feature selection algorithms. The experimental results demonstrate that the proposed BCOVIDOA significantly outperforms the existing algorithms in terms of accuracy, best cost, the average cost (AVG), standard deviation (STD), and size of selected features. Additionally, the Wilcoxon rank-sum test is calculated to prove the statistical significance of the results.

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.

An integrated cuckoo search optimizer for single and multi-objective optimization problems

Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.

Design of shared unit-dose drug distribution network using multi-level particle swarm optimization

Unit-dose drug distribution systems provide optimal choices in terms of medication security and efficiency for organizing the drug-use process in large hospitals. As small hospitals have to share such automatic systems for economic reasons, the structure of their logistic organization becomes a very sensitive issue. In the research reported here, we develop a generalized multi-level optimization method - multi-level particle swarm optimization (MLPSO) - to design a shared unit-dose drug distribution network. Structurally, the problem studied can be considered as a type of capacitated location-routing problem (CLRP) with new constraints related to specific production planning. This kind of problem implies that a multi-level optimization should be performed in order to minimize logistic operating costs. Our results show that with the proposed algorithm, a more suitable modeling framework, as well as computational time savings and better optimization performance are obtained than that reported in the literature on this subject.