Chaotic binary bat algorithm with mutual information for feature subset selection

The quality of service multicast routing problem is a very important research issue for transmission in wireless mesh networks. It is known to be NP-complete problem, so many heuristic algorithms have been employed for solving the multicast routing problem. This paper proposes a modified binary bat algorithm applied to solve the QoS multicast routing problem for wireless mesh network which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate to get low-cost multicasting tree. The binary bat algorithm has been modified by introducing the inertia weight w in the velocity update equation, and then the chaotic map, uniform distribution and gaussian distribution are used for choosing the right value of w. The aim of these modifications is to improve the effectiveness and robustness of the binary bat algorithm. The simulation results reveal the successfulness, effectiveness and efficiency of the proposed algorithms compared with other algorithms such as genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization algorithm, bacteria foraging-particle swarm optimization, bi-velocity discrete particle swarm optimization and binary bat algorithm.

Association rule mining meeting a variety of measures is regarded as a multi-objective optimization problem rather than a single objective optimization problem. The convergent speed of traditional multi-objective algorithms such as genetic algorithm is slow and the efficiency of these algorithms is low. Furthermore, the rules generated by traditional multi-objective algorithms are too large to be efficiently analyzed and explored in any further process. Bat algorithm is a new efficient global optimal algorithm whose convergence is superior to binary particle swarm optimization (BPSO) and genetic algorithm. This paper discusses the application of multi-objective bat algorithm to association rule mining. We propose multi-objective binary bat algorithm (MBBA) based on Pareto for association rule mining. This algorithm is independent of minimum support and minimum confidence. To evaluate the association rules mined by MBBA algorithm, we propose a new method to discover interesting association rules without favoring or excluding any measure. Compared with the single-objective BPSO, binary bat algorithm (BBA) and Apriori algorithm, the experimental results on six datasets show that the new algorithm is feasible and highly effective. It can make up the shortage of single objective algorithms and traditional association rule mining algorithms.

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

Nowadays, along with network development, due to the threats of unknown sources, information communication is more vulnerable, and thus, more secured information is required. Intrusion Detection System (IDS) is very important for cybersecurity with the presence in particular of various networked computers' foundation. An efficient IDS apply machine learning method as computational technics to increase rates of detection to gain the high accuracy and low false alarms rate within the huge amounts of data. To increase the rate of detection, researcher usually implements the optimizer. Thus, in this research, a comprehensive experimental study is presented based on various binary to optimize the rate of detection and decrease the error. Moreover, Numerous researches have been conducted about intrusion detection systems with the old dataset such as Kddcup'99 dataset, and due to this reason, most of them did not get the potential accuracy to detect intrusion with the current intrusion as the old dataset is not covering the current attacks. Therefore, this research aims to A hybrid Anomaly Classification of IDS with Deep Learning (DL) and Binary Algorithms (BA) as Optimizer with the most updated dataset named "CICIDS2017" which can be used for the intrusion detection evaluation. Moreover, this research conducts the anomaly classification of IDS based on the deep neural network (DNN) as the Deep Learning (DL) platform and Binary Algorithms (BA) in terms of Binary Bat Algorithm (BBA), Binary Genetic Algorithm (BGA), Binary Gravitational Search Algorithm (BGSA) as optimizer to enhance the rates of detection. Some of the results which had been considered and achieved for DNN and the hybrid version (DNN and Binary Algorithms) are in terms of: Accuracy, Recall, Precision, Confusion Matrix, Sensitivity, Specificity, and Cost Error.

Biometric authentication systems play a crucial role in ensuring secure access control in the era of the Internet of Things (IoT). Iris recognition, in particular, offers a highly accurate and reliable means of authentication. However, the success of iris recognition heavily relies on the accurate segmentation of the iris region from the surrounding areas. This research article presents a novel approach to iris segmentation using the Binary Bat Algorithm (BAT) in the context of biometric authentication in IoT. The proposed methodology involves preprocessing the iris images, defining an objective function to evaluate the quality of the iris segmentation, encoding the candidate solutions using binary encoding, initializing the population of bat solutions, and incorporating local search mechanisms within the BAT algorithm to fine-tune the segmentation. Experimental evaluations are conducted using a publicly available iris dataset, comparing the proposed BAT algorithm with existing segmentation methods. The performance of the BAT algorithm is assessed using evaluation metrics such as segmentation accuracy, completeness, and computational efficiency. Additionally, the robustness of the BAT algorithm is analyzed under various challenging conditions, including varying lighting conditions, occlusions, and noise. The results demonstrate that the BAT algorithm outperforms existing segmentation methods in terms of accuracy and completeness. The proposed approach shows promising potential for efficient iris segmentation in biometric authentication systems in the IoT domain.

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter [Formula: see text] of the pulse frequency [Formula: see text]. Second, we use a dynamic formulation to update the parameter α of the loudness [Formula: see text]. The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and Binary Bat Algorithm (BBA).

In the era of data, it is a challenging task to classify continuous data such as electroencephalographic data. The electroencephalographic signal maps several thoughts going in an individual’s brain by connecting a device to the human brain. In this paper, we have proposed a deceit identification system using a test called “concealed information test.” The electroencephalographic data have been recorded when the concealed information test is performed for experimental analysis. To enhance the performance of the deceit identification system, the optimization of support vector machine (SVM) parameters and the selection of the EEG channels are performed. This paper implements a binary version of the BAT algorithm (binary BAT algorithm) and the conventional BAT algorithm on the electroencephalography (EEG) data. A novel cost function is also proposed, which utilizes the results of continuous BAT and binary BAT to enhance the system performance. In this synergistic approach, BAT is used for the SVM parameters optimization, and the binary BAT algorithm is applied for the EEG channel selection. The performance of the system is improved, and it is inferred that the channels placed at the occipital lobe of the brain consist of the artifacts. After removing the channels placed on the occipital lobe, i.e., O1, Oz, and O2, and using the optimized SVM parameters, the system’s average accuracy increases from 94.11% to 96.8%.

This paper presents a novel binary bat algorithm (NBBA) to solve 0–1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm enables the bats to enhance the exploration capability while LSS aims to boost the exploitation tendencies and, therefore, it can prevent the BBA–LSS from the entrapment in the local optima. Moreover, the LSS starts its search from BBA found so far. By this methodology, the BBA–LSS enhances the diversity of bats and improves the convergence performance. The proposed algorithm is tested on different size instances from the literature. Computational experiments show that the BBA–LSS can be promise alternative for solving large-scale 0–1 knapsack problems.

DNA microarray analysis plays a prominent role in classifying genes related to cancer. The dimension of the data is high and difficult to handle during classification. Hence, the dimension has to be reduced and highly predictive gene features must be obtained without affecting the accuracy. Previous studies concentrated either on improving the classification accuracy or reduction of gene features. Here, the multi‐objective problem of obtaining reduced gene features with high classification accuracy is addressed using the proposed correlation feature selection filter and binary bat algorithm (BBA) with greedy crossover. The gene feature subsets are obtained using the correlation based feature selection filter and optimized using the BBA. Suboptimal solutions obtained due to pre‐convergence of BBA are reset using the proposed greedy crossover. Highly predictive genes features are obtained and evaluated with support vector machine 10‐fold cross‐validation. An average classification accuracy of 95.85% with predictive gene features <1% of the total dataset was obtained when applied on cancer microarray datasets. The solution for the multi‐objective problem of obtaining high classification accuracy with minimal number of genes is achieved with better performance over the existing algorithms. Also, the problem of pre‐convergence with suboptimal solutions in optimization algorithms is overcome.

Online and offline streaming feature selection methods with bat algorithm for redundancy analysis

Discovering disease-related genes from high-dimensional biomedical data is currently a hot issue. However, most biomedical data has a large number of irrelevant or redundant features, which makes the data difficult to utilize directly. For this problem, an improved binary bat algorithm based on T-test and variable step size adaptive algorithm (TABBA) is proposed. The T-test is employed to generate the initial population. Then a variable step size adaptive algorithm is introduced to accelerate convergence and avoid falling into the local optimum. The performance of TABBA is compared with original binary bat algorithm (BBA), variable step size adaptive binary bat algorithm (ABBA) and other optimization algorithms. The results in four public biomedical datasets confirm that TABBA is superior compared to benchmark methods in term of accuracy and the number of selected features.

A wrapper based binary bat algorithm with greedy crossover for attribute selection

A novel test-cost-sensitive attribute reduction approach using the binary bat algorithm

In this study, the binary bat algorithm (BBA) for structural topology optimization is implemented. The problem is to find the stiffest structure using a certain amount of material and some constraints using the bit-array representation method. A new filtering algorithm is proposed to make BBA find designs with no separated objects, no checkerboard patterns, less unusable material, and higher structural performance. A volition penalty function for topology optimization is also proposed to accelerate the convergence toward the optimal design. The main effect of using the BBA lies in the fact that the BBA is able to handle a large number of design variables in comparison with other well-known metaheuristic algorithms. Based on the numerical results of four benchmark problems in structural topology optimization for minimum compliance, the following conclusions are made: (1) The BBA with the proposed filtering algorithm and penalty function are effective in solving large-scale numerical topology optimization problems (fine finite elements mesh). (2) The proposed algorithm produces solid-void designs without gray areas, which makes them practical solutions that are applicable in manufacturing.

Bat algorithm (BA) is one of the most recent bio-inspired algorithm. It is based on the echolocation behavior of microbats. The standard BA is proposed only for continuous optimization problems. In this paper, we try to solve the graph coloring problem using a binary bat algorithm. To show the feasibility and the effectiveness of the algorithm, we have used the DIMACS benchmark, and the obtained results are very encouraging.