A Novel Hybrid Binary Bat Algorithm for Global Optimization

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.

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.

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).

Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen well-known datasets provided by the UCI repository were utilized to show the significance of the proposed approach. The reported results show that BHHO-TVS outperforms BHHO with traditional binarization schemes as well as other binary feature selection methods such as binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary whale optimization algorithm (BWOA), and binary salp swarm algorithm (BSSA). Compared with other similar feature selection approaches introduced in previous studies, the proposed method achieves the best accuracy rates on 67% of datasets.

Bat Algorithm (BA) is a recently proposed heuristic algorithm based on the echolocation behavior of bats. BA has proven to have better performance than other well-known algorithms like particle swarm optimization (PSO) and genetic algorithm (GA). However, the research on using BA for binary and multi-objective optimization has just begun. In this paper we propose a multi-objective binary bat algorithm (MBBA) for multi-objective optimization in binary search space. The algorithm uses a modified bat position updating strategy which works better with binary problems, a mutation operator is introduced to improve the local search ability and help the diversity of algorithm, a Pareto dominance based approach with external elitist archive to find optimal Pareto solutions, and a flight leader selection approach to help bat ight. An experimental study is done on multiple multi-objective benchmarks to test the performance of the proposed algorithm and compare it to the performance of non-dominated sorting genetic algorithm II (NSGA-II). The experimental results show that the proposed MBBA is a competitive multi-objective algorithm and outperforms NSGA-II.

Voltage dips represent a significant power quality problem. The main cause of voltage dips and short-term interruptions is an electrical short circuit that occurs in transmission or distribution networks. Faults in the power system are stochastic by nature and the main cause of voltage dips. As faults in the transmission system can affect more customers than faults in the distribution system, to reduce the number of dips, it is not enough to invest in a small part of the transmission or distribution system. Only targeted investment in the whole (or a large part of the) power system will reduce voltage dips. Therefore, monitoring parts of the power system is very important. The ideal solution would be to cover the entire system so that a power quality (PQ) monitor is installed on each bus, but this method is not economically justified. This paper presents an advanced method for determining the optimal location and the optimal number of voltage dip measuring devices. The proposed algorithm uses a monitor reach area matrix created by short-circuit simulations, and the coefficient of the exposed area. Single-phase and three-phase short circuits are simulated in DIgSILENT software on the IEEE 39 bus test system, using international standard IEC 60909. After determining the monitor reach area matrix of all potential monitor positions, the binary bat algorithm with a coefficient of the exposed area of the system bus is used to minimize the proposed objective function, i.e., to determine the optimal location and number of measuring devices. Performance of the binary bat algorithm is compared to the mixed-integer linear programming algorithm solved by using the GNU Linear Programming Kit (GLPK).

Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic Algorithm, Particle Swarm Optimization with Relative Reduct, Quick Reduct and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. In this paper we proposed a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function. The proposed method is then tested with various classification algorithms like decision tree, multilayer perceptron, support vector machine and clustering quality measures like sum of squared error. The results show that our proposed method gives more accuracy when compared with other optimization algorithm.

Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm

In this paper, Security Constrained Unit Commitment (SCUC) problem with combined Thermal- Solar generating units is solved using Artificial Bee Colony (ABC) algorithm. SCUC problem aims to obtain minimum generation cost while satisfying the system, network and security constraints. The SCUC problem is divided into Unit Commitment Problem (UCP) which is considered as the master problem and Security Constrained Economic Dispatch (SCED) is considered as the sub problem. Here, both thermal and solar generations are considered in UCP and SCED solutions. Binary Coded ABC Algorithm is used to solve the UCP and Real Coded ABC Algorithm is used for solving the SCED problem. The constraints such as spinning reserves, hourly load demands, ramp up/ramp down limits of generators, minimum up/down limits, line flow and voltage limits are considered in the problem formulation. Beta distribution function is employed to forecast the solar power generation in different seasons. The comparison is carried out with and without incorporating solar generation. The proposed method is validated on a 6 bus system and a South Indian 86 bus utility.

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits.

The invention of phasor measurement unit (PMU) is considered to be one of the most important breakthroughs in the field of electrical grids monitoring and control. PMUs can be used to obtain accurate voltage and current phasors' measurements. The high cost of installation related to PMU limited its usage and increased the need for optimal allocation of PMUs. A binary bat optimization algorithm is proposed in this paper for solving the optimal PMU placement (OPP) problem to obtain a fully observable system. Simulations are carried on various IEEE bus test systems to ensure the algorithm's effectiveness and feasibility. A comparison is made between the obtained results and the other algorithms' results. The execution time of the proposed algorithm is also compared with other algorithms to illustrate the algorithm's ability to obtain the problem solution with lower execution time and better convergence curve.

Reducing the energy consumption of nodes and prolonging network lifetime is a key issue in wireless sensor networks (WSN) research field. Clustering is an effective method to solve this problem. However, the effectiveness of existing clustering algorithms is not satisfactory. Therefore, we propose a binary bat algorithm based clustering approach (BBACA) in this paper. Firstly, the clustering of WSN is abstracted into a combinatorial optimization problem which is solved by using binary bat algorithm (BBA) with excellent performance. Secondly, a novel binary artificial bat encoding scheme and a fitness function using multiple parameters are designed elaborately. Finally, the final clustering scheme of WSN is obtained through multiple iterations of BBA. Simulation results show that BBACA can significantly reduce the energy consumption of nodes, extend the lifetime of the network, and achieve an outstanding clustering effect compared with other clustering algorithms.

DisABC: A new artificial bee colony algorithm for binary optimization

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.

Network lntrusion Detection always faces challenges in creating a classifier that can handle the distribution of attack sequences in the KDDCup99 datasets. The attacks distribution in the training data is reasonable in the non-frequent categories compared to the entire population. As a result, categories and simultaneously, resulting in a poor detection rate. Selection of feature is a challenging task in wide range of data and choosing a customized subset of features always leads to better performance. To increase the detection rate of rare categories and improve overall performance, cascade classifiers are trained by using the partitioned training dataset and rare attack classes are separated by major categories. The “Binary Bat Algorithm” is used for feature selection technique. The proposed system has been implemented in the Hadoop framework by using MapReduce to improve execution time. The developed system is then explored with the classification algorithms such as the Naive Bayesian classifier and J48. The empirical result indicates that the proposed method achieves higher detection rates and high accuracy than existing methods.