Binary Cuckoo Search Research Articles

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

High-dimensional datasets often harbor redundant, irrelevant, and noisy features that detrimentally impact classification algorithm performance. Feature selection (FS) aims to mitigate this issue by identifying and retaining only the most pertinent features, thus reducing dataset dimensions. In this study, we propose an FS approach based on black hole algorithms (BHOs) augmented with a mutation technique termed MBHO. BHO typically comprises two primary phases. During the exploration phase, a set of stars is iteratively modified based on existing solutions, with the best star selected as the “black hole”. In the exploration phase, stars nearing the event horizon are replaced, preventing the algorithm from being trapped in local optima. To address the potential randomness-induced challenges, we introduce inversion mutation. Moreover, we enhance a widely used objective function for wrapper feature selection by integrating two new terms based on the correlation among selected features and between features and classification labels. Additionally, we employ a transfer function, the V2 transfer function, to convert continuous values into discrete ones, thereby enhancing the search process. Our approach undergoes rigorous evaluation experiments using fourteen benchmark datasets, and it is compared favorably against Binary Cuckoo Search (BCS), Mutual Information Maximization (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Eelevance (mRMR), approaches. The results demonstrate the efficacy of our proposed model in selecting superior features that enhance classifier performance metrics. Thus, MBHO is presented as a viable alternative to the existing state-of-the-art approaches. We make our implementation source code available for community use and further development.

A hybrid variable selection method combining Fisher's linear discriminant combined population analysis and an improved binary cuckoo search algorithm.

In this paper, a novel hybrid variable selection method for model building by near-infrared (NIR) spectroscopy is proposed for composition measurement in industrial processes. A double-layer structure is designed for variable selection by combining Fisher's linear discriminant combined population analysis (FCPA) and an improved binary cuckoo search algorithm (IBCS). The Fisher classifier combined with model population analysis is used to select the variable interval wherein the useful variables are roughly located even when strong multicollinearity exists among spectral variables. Opposition-based learning (OBL) and jumping genes (JG) are introduced to improve the binary cuckoo search algorithm for the fine selection of key variables, thus avoiding the loss of excellent solutions due to randomness and the local optimum. Different variable selection methods were used to select variables for beer, corn, and diesel fuel datasets, and the partial least squares (PLS) algorithms were used to build calibration models to predict the original extract concentration of beer, the protein and starch content of corn, and the boiling point of diesel fuel, respectively. The results showed that the proposed PLS modeling method based on FCPA-IBCS has higher fitting accuracy and smaller prediction errors.

MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics

MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics

Corrections to “MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics”

Corrections to “MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics”

ODM-BCSA: An Offloading Decision-Making Framework based on Binary Cuckoo Search Algorithm for Mobile Edge Computing

Computational task offloading facilitates real-time applications on constrained mobile devices that require a large amount of processing resources, high storage capacity and battery power. Mobile Edge Computing (MEC) is a computing paradigm that shifts computing resources closer to the user at the network’s edges. Heavy tasks are then offloaded to edge nodes, thereby reducing the computations required on the mobile side. However, offloading computational tasks may result in additional energy consumption and delays, due to network congestion and time spent in server queues. Thus, to minimize completion time and energy consumption, it is essential to optimize offloading decisions, while also considering the financial costs. In this paper, we propose an Offloading Decision-Making Framework based on the Binary Cuckoo Search Algorithm for Mobile Edge Computing (ODM-BCSA). We formulated an offloading problem as a mixed-integer optimization problem to minimize time, energy, and payment costs. We resolved the problem of resource allocation using the Binary Cuckoo Search Algorithm (BCSA). The simulation results revealed that offloading decisions depend on multiple parameters, including the number of mobile devices being handled by the edge server, the bandwidth, and the number of tasks. Decisions made also depended on the priority assigned to each objective. Finally, we compared the ODM-BCSA against a brute force search, proving that the ODM-BCSA is more efficient and greatly minimizes execution time when a huge number of mobile devices are involved (by 99.9 %).

Weight-imposed Elite Hybrid Binary Cuckoo Search (EHBCS)-based clustering for improving network lifetime longevity

In Wireless Sensor Networks (WSNs), Clustering aids in maximizing the lifetime of the network with sustained energy stability in the sensor nodes during data dissemination. In this clustering process, the sensor nodes are organized into clusters with the potential fitness node designated as Cluster Heads (CHs) for collecting and forwarding the data to the sink. In specific, the energy consumption of sensor nodes during their role as CH is maximized with great impact over the network lifespan. In this paper, a Weight-imposed Elite Hybrid Binary Cuckoo Search (EHBCS)-based Clustering Mechanism is proposed for facilitating potent data transmission with minimized energy consumption and improved network lifetime. This EHBCS is proposed as a novel energy-sensitive CH selection framework based on the process of hierarchical routing through the inclusion of hybrid optimization algorithm. It selected CH depending on the parameters of Quality of Service (QoS), delay, distance, and energy into account. It integrated the merits of Binary Cuckoo Search and Elite Mechanism for selecting CHs and performing effective processes by preventing sinkhole issues in WSNs. The results of EHBCS confirmed better throughout by 11.32%, minimized energy consumption by 13.84%, and minimized delay by 16.12% with an increasing number of sensor nodes, compared to the baseline CH selection approaches used for exploration.

Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search

In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology to extract fault features. Second, a clustering hybrid initialization technique is given for feature selection, combining the Louvain algorithm and the feature number. Third, a mutation strategy based on Levy flight is proposed, which effectively utilizes high-quality information to guide subsequent searches. In addition, a dynamic abandonment probability is proposed based on population sorting, which can effectively retain high-quality solutions and accelerate the convergence of the algorithm. Experimental results from nine UCI datasets show the effectiveness of the proposed improvement strategy. The open-source bearing dataset is used to compare the fault diagnosis accuracy of different algorithms. The experimental results show that the diagnostic error rate of this method is only 1.13%, which significantly improves classification accuracy and effectively realizes feature dimension reduction in fault datasets. Compared to similar methods, the proposed method has better comprehensive performance.

Collaboration of features optimization techniques for the effective diagnosis of glaucoma in retinal fundus images

Glaucoma is the second most common cause of vision loss. Manual screening of a patient's eye or screening through a fundus image of the patient's eye requires expert ophthalmologists. This screening analysis is time-consuming, requires expert human involvement, and is subject to human intra-observer variability. Thus, medical imaging professionals are working to solve these issues by investigating retinal images for glaucoma detection using artificial intelligence-based computer-aided diagnosis systems (CAD). Machine learning algorithms (for classification) and nature-inspired computing (for feature selection/reduction) embedded CAD systems can successfully identify retinal pictures and can be employed to overcome these challenges. This proposed work is a productive attempt in which we have proposed two novel two-layered approaches (BA-BCS, BCS-PSO) which are based on Particle Swarm Optimization (PSO), Binary Cuckoo Search (BCS), and Bat Algorithm (BA). We have also analyzed the performances of BA, BCS, and PSO separately. These five (single and two-layered) approaches are used to compose subsets of reduced features that can generate the maximum accuracy when forwarded to three machine learning classifiers. Benchmark publicly available datasets, ORIGA and REFUGE, and their combinations are used to validate the proposed methodology. A maximum accuracy of up to 98.95% is achieved using these approaches. Apart from this, many other trade-off solutions are also suggested for the researcher's community. This study therefore presents novel efforts with new and efficient results that are beneficial to ophthalmologists, researchers, and humanity.

Course scheduling algorithm based on improved binary cuckoo search

Course scheduling algorithm based on improved binary cuckoo search

A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

An Application of Binary Cuckoo Search Algorithm to Orienteering Problem

This research applies the cuckoo search metaheuristics model to find solutions to the Orienteering Problem (OP). The OP formulation is useful to model a situation in which someone wants to determine an optimal city route that is subject to a specified time constraint. OP can be categorized into NP Hard Problem which takes a very long time to analytically find the optimal solution as the number of entities involved increases. Therefore, metaheuristics often become an option to deal with this situation. A cuckoo search model based algorithm is developed in this research. An adjustment for discrete combinatorial problem is performed by adopting an idea of binary cuckoo search method. In addition, three types of local search methods are considered to improve the searching performance. This algorithm can eventually find better solutions for some of the 18 cases than two other benchmarked algorithms. Furthermore, experiment on model parameters shows that the worse nest fraction (P_alpha) affects the quality of solutions obtained.

Feature Selection Method Based on Grey Wolf Optimization and Simulated Annealing

Background: Feature selection sometimes also known as attribute subset selection is a process in which optimal subset of features are elected with respect to target data by reducing dimensionality and removing irrelevant features. There will be 2n possible solutions for a dataset having n number of features that is difficult to solve by conventional attribute selection method. In such cases metaheuristic-based methods generally outruns the conventional methods. Objective: The main aim of this paper is to enhance the classification accuracy and minimize the number of selected features and error rate. Methods: To achieve the objective, a binary metaheuristic feature selection method bGWOSA based on grey wolf optimization and simulated annealing has been introduced. The proposed feature selection method uses simulated annealing for equalizing the trade-off between exploration and exploitation. The performance of the proposed binary feature selection method has been examined on the ten feature selection benchmark datasets taken from UCI repository and compared with binary cuckoo search, binary particle swarm optimization, binary grey wolf optimization, binary bat algorithm and binary hybrid whale optimization method. Results: The proposed feature selection method achieves the highest accuracy for the most of datasets compared to state-of-the-art. Further, from the experimental and statistical results, efficacy of the proposed feature selection method has been validated. Conclusion: Classification accuracy can be enhanced by employing feature selection methods. Moreover, performance can also be enhanced by tuning the control parameters of metaheuristic methods.

Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification.

For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature selection on a series of binary classification datasets, including low-dimensional and high-dimensional samples by SVM classifier. The experimental results show that the EHBCS algorithm achieves better classification performances compared with binary genetic algorithm and binary particle swarm optimization algorithm. Besides, we explain its superiority in terms of standard deviation, sensitivity, specificity, precision, and F-measure.

Brain MR image tumor detection and classification using neuro fuzzy with binary cuckoo search technique

AbstractBrain tumor and stroke are two important causes of death in and around the world. Tumor classification and retrieval system plays a vital role in medical field. Tumor detection, segmentation and MR imaging seizures are a major concern, although it can be a daunting and tedious task for clinical specialists, the accuracy of which depends solely on their experience. In this article, the neuro fuzzy with binary cuckoo search optimization method is proposed for detecting tumors on MR images. The method has four stages. In the first step, raw MR images are pre‐processed by the anisotropic filter, and in the second phase, the removal of the skull is classified by type. The third phase involves the functioning of singular value decomposition and principle component analysis. Finally, the NFBCS method is used to detect and classify tumors and the BCS algorithm optimizes the study model for better classification accuracy.

Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features

In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a fully automatic method based on deep learning model and handcrafted features is presented for the detection of the lumen borders in IVUS images. First, 193 handcrafted features are extracted from the IVUS images. Then hybrid feature vectors are constructed by combining handcrafted features with 64 high-level features extracted from U-Net. In order to obtain the feature subsets with larger contribution, we employ the extended binary cuckoo search for feature selection. Finally, the selected 36-dimensional hybrid feature subset is used to classify the test images using dictionary learning based on kernel sparse coding. The proposed algorithm is tested on the publicly available dataset and evaluated using three indicators. Through ablation experiments, mean value of the experimental results (Jaccard: 0.88, Hausdorff distance: 0.36, Percentage of the area difference: 0.06) prove to be effective improving lumen border detection. Furthermore, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy.

Enhanced Binary Cuckoo Search With Frequent Values and Rough Set Theory for Feature Selection

Redundant and irrelevant features in datasets decrease classification accuracy, and increase computational time of classification algorithms, overfitting problem and complexity of the underlying classification model. Feature selection is a preprocessing technique used in classification algorithms to improve the selection of relevant features. Several approaches that combine Rough Set Theory (RST) with Nature Inspired Algorithms (NIAs) have been used successfully for feature selection. However, due to the inherit limitations of RST for some data types and the inefficient convergence of NIAs for high dimensional datasets, these approaches have mainly focused on a specific type of low dimensional nominal dataset. This paper proposes a new filter feature selection approach based on Binary Cuckoo Search (BCS) and RST, which is more efficient for low and high dimensional nominal, mixed and numerical datasets. It enhances BCS by developing a new initialization and global update mechanisms to increase the efficiency of convergence for high dimensional datasets. It also develops a more efficient objective function for numerical, mixed and nominal datasets. The proposed approach was validated on 16 benchmark datasets; 4 nominal, 4 mixed and 8 numerical drawn from the UCI repository. It was also evaluated against standard BCS; five NIAs with fuzzy RST approaches; two popular traditional FS approaches; and multi objective evolutionary, Genetic, and Particle Swarm Optimization (PSO) algorithms. Decision tree and naive Bayes algorithms were used to measure the classification performance of the proposed approach. The results show that the proposed approach achieved improved classification accuracy while minimizing the number of features compared to other state-of-the-art methods. The code is available at https://github.com/abualia4/EBCS .

Multimodality medical image fusion based on non-sub-sampled contourlet transform

The fusion of medical images is an essential and effective technique for disease analysis. The current study proposed a Non Sub sampled Contourlet Transform (NSCT) image fusion technique in which Neuro Fuzzy with Binary Cuckoo Search (NFBCS) and Salp Swarm Optimisation (SSO) methods were utilised. The researchers successfully fused the images retrieved from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) and created a single merged image which becomes a new integrated diagnostic method. At the beginning, two unique sets of images such as MRI and CT were considered for the fusion procedure. These pairs of images are used in NSCT to generate the image and divide it into high frequency module and low-frequency module. Mixing policies are used here to generate and combine high- and low-frequencies. Compared to existing techniques, the results of the proposed technical tests exhibited better processing efficiency and delivered excellent results on subjective and objective evaluation criteria. This is particularly advantageous for accurate clinical analysis of the disease. The proposed work was carried out in MATLAB program. The results were tested on the medical images of brain and spine. The outcomes were compared in terms of MSE, RMSE and entropy.

Hybrid mechanism for medical data classification

In recent years, medical data mining is the hot topic of research. Earlier extraction was done manually so the process was time consuming. Thus, to solve this drawback machine learning technique was implemented to extract knowledge from the large dataset. In this paper, the medical data processed for removal of missing values, then orthogonal local preserving projection (OLPP) algorithm is employed for feature dimension reduction. Then, fuzzy rules are generated and optimised by binary cuckoo search algorithm. Further, the classification is carried out using the generated fuzzy rules and the best rule is selected based on the accuracy using the decision tree classifier. The performance of the proposed technique has been evaluated using University of California Irvine (UCI) datasets namely Cleveland, Hungarian, Mammographic masses, PID and Switzerland. After observation and comparison, the method achieves highest accuracy of 97.3% for PID dataset whereas the existing method provides 89.5% accuracy for PID dataset.

Speech feature selection and emotion recognition based on weighted binary cuckoo search

In this paper, a hybrid system is proposed for speech emotion recognition (SER). The algorithm in this paper adopts a two-stage design concept. In the first stage, we use the ensemble learning model random forest algorithm to obtain the importance of each feature. We use Emo-DB for experimental comparison and find that the combination of the logistic regression algorithm and the WBCS algorithm achieves best results. Cross-training method is used to ensure the features adapt to various situations. Under 100 training sets, the sentiment classification results are satisfactory. The proposed method is more accurate than state-of-the-art intelligent optimization dimensionality reduction algorithms.

Optimization of electric distribution network configuration for power loss reduction based on enhanced binary cuckoo search algorithm

This paper presents a new network reconfiguration (NR) method using enhanced binary cuckoo search algorithm (EBCSA) to reduce power loss in the distribution system. Enhanced binary cuckoo search algorithm (EBCSA) is developed by transferring the continuous cuckoo search algorithm (CSA) to the binary domain and adding a new local search mechanism. Calculation results on different test systems demonstrate that EBCSA has capability for searching the optimal network configuration with greater success rate, better optimal solution quality and smaller number of average convergence iterations than those of binary CSA, binary coyote optimization algorithm, binary genetic algorithm and binary particle swarm optimization. Furthermore, the effect of the transfer function transferring from continuous domain to binary domain and the value of the parameters of EBCSA for the NR problem are also evaluated to choose the suitable values. The results show that EBCSA is a tool worth considering for the NR problem.