Binary Optimization Algorithm Research Articles

Novel designs of blade mixer impellers from the discrete element method and topology optimization

Granular mixers are essential for manufacturing products that require bulk materials as precursors. The modeling of mixers is aided by the discrete element method (DEM), which can simulate the flow of bulk material. DEM is applied in mixer design studies by varying a few design parameters and analyzing the predicted performance, but systematic design via optimization methods has yet to be performed. In this work, we utilized a combined DEM and topology optimization approach to design blade mixer impellers for improved mixing and reduced energy consumption. A DEM model was formulated from a blade mixer setup and validated with experimental particle flow data. The computational cost of the model was then reduced by employing larger particle sizes in the simulations. A binary genetic optimization algorithm was used afterwards to find the tilt angle, number, and shape of impeller blades that would improve mixing or reduce the required driving power. The high mixing impellers have four blades that each have cavities at the center and the tip. The increased blade count promotes convective mixing, while the cavities enhance shear mixing. Meanwhile, the low energy impellers have two blades that both have cavities at the center and less material at the blade tips. These minimize particle-impeller collisions, which in turn reduce the power required to drive the impeller. This work demonstrates the use of optimization methods to design the shape of impeller blades – a variable often overlooked in conventional DEM studies. Our approach may be used to systematically design other mixer types as well.

High Directivity Microstrip Patch Antenna design using Binary Ebola Search Optimization for Radio Frequency Identification Application

Microstrip patch antenna has gained significant popularity in wireless communication field, because of its compact size, less profile, and ease of installation. It is widely used in various applications such as Radio-Frequency Identification (RFID) systems, where dependable and long-range communication is made possible by the great directivity and efficiency. In this manuscript, a High Directivity Microstrip Patch Antenna design using Binary Ebola Search Optimization for Radio Frequency Identification Application (MPA-RFID-BESO) is proposed.The design of microstrip patch antennas is the difficulty in achieving high directivity while maintaining compact size and low profile. This research explains design and application-specific optimisation of the Microstrip Patch Antenna (MPA) of biomedical utilizing Binary Ebola Search optimization (BESO) algorithm. Microstrip patch antenna is designed to function in double and multi band applications due to its low cost, light weight, and ease of installation. MPA is made with flawed ground construction to lessen cross-polarized radiation emitted by microstrip patches and to achieve the necessary radiation parameters. Here, the cost of the materials is reduced by using a liquid crystal polymer substrate, and aerial performance is enhanced by using the appropriate geometrical parameters. The small design of the BESO optimised improves the performance of antenna as 50mm×50mm compact size. The MATLAB program uses high frequency to do the simulation method. The proposed antenna design is a preferable option for applications involving Radio Frequency Identification (RFID). The performance metrics, such as radiation pattern, constant gain, directivity, bandwidth, and antenna efficiency and return loss are measured and compared to the existing techniques. The proposed MPA-BA-BESO design provides 10.51%, 12.85% and 16.04% higher bandwidth and 23.63%, 47.86% and 32.06% higher gain compared with existing designs, like Designing MPA utilizing Moth–Flame optimization approach (MPA-UWB-MFOA), Predicting Rectangular Patch Microstrip Antenna Dimension utilizing Machine Learning (MPA-ANN), Dual Band Antenna Design and Prediction of Resonance Frequency Utilizing Machine Learning Algorithms (DBAD-RF-CNN) respectively. The proposed technology serve as a better design alternative for microstrip patch antennas in communication systems to address biological applications.

Rap-Densenet Framework for Network Attack Detection and Classification

Cybersecurity is becoming increasingly important with the rise in Internet usage. The two most frequent cyberattacks that can seriously harm a website or a server and render them inaccessible to other customers are denial of service (DoS) and distributed denial of service (DDoS) attacks. These attacks are so common and take many different forms, it is difficult to identify and respond to them with previous methods. Furthermore, computational complexity, inconsistency, and irrelevant data are problems for traditional intrusion detection methods. As a result, a powerful deep learning-based technique is applied in this study for the identification and categorisation of DoS and DDoS attacks. Refined Attention Pyramid Network (RAPNet)-based feature extraction is used in this proposed framework to extract features from the input data. Then, Binary Pigeon Optimisation Algorithm (BPOA) is used to determine the best features. After choosing optimal characteristics, Densenet201-based deep learning is deployed to categorise the assaults in Bot-IoT, CICIDS2017, and CICIDS2019 datasets. Furthermore, the Conditional Generative Adversarial Network (CGAN) is used to provide extra data samples for minority classes to address the issue of imbalanced data. The findings show that the proposed model can precisely identify and categorise DoS and DDoS assaults in comparison to the existing intrusion detection approaches with 99.43%, 99.26%, and 99.38% accuracy for CICIDS2019, CICIDS2017, and BoT-IoT correspondingly.

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection.

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine

There are hundreds of various sensors used for online Prognosis and Health Management (PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for inflight control purposes in LRE, it is practical to optimal placement of redundant sensors for improving the diagnosability and economics of PHM systems. To strike a balance between sensor cost, real-time performance and diagnosability of the fault diagnosis algorithm in LRE, this paper proposes a novel Optimal Sensor Placement (OSP) method. Firstly, a Kernel Extreme Learning Machine-based (KELM) two-stage diagnosis algorithm is developed based on a system-level failure simulation model of LRE. Secondly, hierarchical diagnosability metrics are constructed to formulate the OSP problem in this paper. Thirdly, a Hierarchy Ranking Evolutionary Algorithm-based (HREA) two-stage OSP method is developed, achieving further optimization of Pareto solutions by the improved hypervolume indicator. Finally, the proposed method is validated using failure simulation datasets and hot-fire test-run experiment datasets. Additionally, four classical binary multi-objective optimization algorithms are introduced for comparison. The testing results demonstrate that the HREA-based OSP method outperforms other classical methods in effectively balancing the sensor cost, real-time performance and diagnosability of the diagnosis algorithm. The proposed method in this paper implements system-level OSP for LRE fault diagnosis and exhibits the potential for application in the development of reusable LREs.

Compact and low-insertion-loss polarization beam-splitting multimode filter using pixelated waveguides.

A polarization beam-splitting multimode filter using pixelated waveguides has been presented and experimentally demonstrated in this paper. Finite difference time domain method and direct binary search optimization algorithm are employed to optimize pixelated waveguides to realize compact size, broad bandwidth, large extinction ratio, low insertion loss, and good polarization extinction ratio. Measurement results show that, in a wavelength range from 1520 to 1560 nm, for the fabricated device working at transverse-electric polarization, the measured insertion loss is less than 1.23 dB and extinction ratio is larger than 15.14 dB, while for transverse-magnetic polarization, the corresponding insertion loss lower than 0.74 dB and extinction ratio greater than 15.50 dB are realized. The measured polarization extinction ratio larger than 15.02 dB is achieved. The device's length is only 15.4 µm.

A Binary Chaotic Transient Search Optimization Algorithm for Enhancing Feature Selection

A Binary Chaotic Transient Search Optimization Algorithm for Enhancing Feature Selection

Improved Binary Meerkat Optimization Algorithm for efficient feature selection of supervised learning classification

Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims to remove redundant and uncorrelated features, thus improving the accuracy of models. However, it can be challenging to select the optimal features, especially for datasets with many features. This paper proposes the Binary Meerkat Optimization Algorithm (BMOA) to address the issue of FS. The BMOA selects the most related and useful features. Additionally, an improved BMOA (IBMOA) is introduced, which enhances exploration and exploitation capabilities by incorporating the Periodic Mode Boundary Handling (PMBH) strategy and the Local Search (LS) process. This helps to reduce dimensionality and enhance classification accuracy. To evaluate the significance of selected features, two widely used classifiers, namely k-nearest Neighbor (k-NN) and Support Vector Machine (SVM), were used as quality raters. The proposed IBMOA and the original BMOA were compared on 21 multi-scale and multi-faceted benchmark datasets. The binary structures of eight contemporary algorithms, including Binary Artificial Bee Colony (BABC), Binary Grey Wolf Optimization (BGWO), Binary Sailfish Optimizer (BSFO), Binary Particle Swarm Optimizer (BPSO), Binary Bird Swarm Algorithm (BBSA), Binary Whale Optimization Algorithm (BWOA), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Bat Algorithm (BBA), were also analyzed and compared. The proposed IBMOA algorithm outperforms competitors on small and large datasets, obtaining optimal solutions within a reasonable timeframe. This is true for the proposed IBMOA, which has been statistically proven to be highly competitive using the Wilcoxon rank sum test (with alpha=0.05).

BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection

BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection

A new binary arithmetic optimization algorithm for uncapacitated facility location problem

A new binary arithmetic optimization algorithm for uncapacitated facility location problem

Constrained Binary Optimization Approach for Pinned Node Selection in Pinning Control of Complex Dynamical Networks

In complex dynamical networks, pinning control techniques are often applied to control a small fraction of the nodes in order to stabilize the network with reduced control effort and energy, facilitating adequate development of the complex network. Selecting the controlled nodes is a key challenge to achieving optimal performance. Theoretical analysis of the network provides the minimum quantity of nodes to control but does not specify which ones should be controlled. Analytically, controllability analysis of the entire network would be ideal, but this becomes difficult for complex networks with a large number of nodes and non-linear dynamics. Another option is to evaluate all possible combinations with the minimum number of necessary nodes or the nodes that can be controlled, but this presents a computational challenge due to the large number of possible combinations. Therefore, the remaining option is the use of metaheuristic algorithms for the rapid and practical evaluation of these combinations. In this work, we propose to optimize the selection of nodes for pinning control based on binary optimization algorithms, subject to control and development constraints. The proposed approach involves finding a binary combination with a fixed number of controlled nodes that best stabilizes the network state to zero. This paper includes a comparative study among state-of-the-art binary optimization algorithms and modified classic optimization algorithms. The applicability of the proposed approach is validated through simulations considering a dynamical discrete-time complex network.

A new binary coati optimization algorithm for binary optimization problems

A new binary coati optimization algorithm for binary optimization problems

Efficient Feature Selection in High Dimensional Data Based on Enhanced Binary Chimp Optimization Algorithms and Machine Learning

Feature selection with the highest performance accuracy is the biggest win for multidimensional data. The Chimpanzee Optimization Algorithm (ChOA) serves as a crucial technique for dealing with multidimensional global optimization issues. However, ChOA often lacks fast convergence and good selection of sensitive attributes leading to poor performance. To address these issues, most significant features were selected using two variants of ChOA called BChimp1 and BChimp2 (BChimp1 and BChimp are available at : https://www.mathworks.com/matlabcentral/fileexchange/133267-binary-chimpoptimization-algorithm-forfeatures-selection. September 22, 202). BChimp1 selects the optimal solution from the four best possible solutions and it applies a stochastic crossover on four moving solutions to deeply speed-up convergence level. BChimp2 uses the sigmoid function to select the significant features. Then, these features were trained using six-well known classifiers. The proposed techniques tend to select the most significant features, speed up the convergence rate and decrease training time for high-dimensional data. 23 standard datasets with six well-known classifiers were employed to assess the performance of BChimp1 and BChimp2. Experimental results validate the efficiency of BChimp1 and BChimp2 in enhancing accuracy by 83.83% and 82.02%, and reducing dimensionality by 42.77% and 72.54%, respectively. However, time-evaluation results of BChimp1 and BChimp2 in all datasets showed fast convergence and surpassed current optimization algorithms such as PSO, GWA, GOA, and GA.

Billiard based optimization with deep learning driven anomaly detection in internet of things assisted sustainable smart cities

Internet of Things (IoT) technology involves a network of interconnected devices and sensors that gather and exchange information. In smart cities, IoT devices were utilized in several fields including energy, transportation, waste management, healthcare, etc., to improve the overall quality of life and sustainability of the populace. But, as the usage of IoT increases, the cybersecurity and data privacy become a concern of safety. An anomaly detection system helps to identify possible data breaches or cyber-attacks by identifying abnormal data patterns. Deep learning (DL) driven anomaly detection has emerged as an effective and powerful method for identifying abnormal behaviours or patterns in the data domain. This technique leverages the abilities of a deep neural network for automated learning of complex patterns and representations from data, which make it better for anomaly detection task where irregularities cannot be easily defined by handcrafted rules. This paper establishes a new Billiard Based Optimization with Deep Learning Driven Anomaly Detection and Classification (BBODL-ADC) technique in IoT-assisted Sustainable Smart Cities. The goal of the BBODL-ADC technique lies in the proper recognition and classification of anomalies in the IoT-assisted smart city. To obtain that, the BBODL-ADC system applies a binary pigeon optimization algorithm (BPEO) algorithm for the effectual selection of features. Besides, the BBODL-ADC technique utilizes Elman recurrent neural network (ERNN) approach for the recognition and classification of anomalies. Moreover, the BBO system can be used for better parameters chosen by the ERNN algorithm. The stimulation value of the BBODL-ADC algorithm can be executed benchmark database. The achieved outcomes demonstrate the remarkable outcome of the BBODL-ADC methodology of 95.69% and 99.21% compared to existing models under dataset-1 and dataset-2.

Binary Arithmetic Optimization Algorithm with Machine Learning based Intrusion Detection System

Intrusion Detection Systems (IDS) are significant for preventing and identifying malicious actions in computer networks. Machine Learning (ML) approaches are extremely executed for recognizing intrusion since it is investigating huge volumes of network traffic data and recognize designs indicative of intrusions. But, the performance of these ML approaches is greatly dependent upon the choice of relevant features which efficiently represent the network traffic data. Feature Selection (FS) is the procedure of recognizing the most informative and discriminative aspects in a given database. As part of Intrusion Detection (ID) utilizing ML, FS purposes for identifying the subset of features that are efficiently differentiated between normal network behaviour and malicious activities. This article proposes a Binary Arithmetic Optimization Algorithm with Machine Learning based Intrusion Detection System (BAOA-MLIDS) technique. The BAOA-MLIDS technique employs FS with an optimal ML classifier for the ID process. To accomplish this, the BAOA-MLIDS technique performs data preprocessing to scale the input data. Besides, the BAOA-MLIDS technique comprises BAOA based FS approach to choose optimal features. Moreover, Extreme Learning Machine (ELM) approach is utilized for the identification of the intrusions. Furthermore, Hunger Games Search Optimization (HGSO) approach was employed for the hyperparameter optimization of the ELM approach. The performance assessment of the BAOA-MLIDS model was examined on a standard dataset and the outputs outperformed the advancement of the BAOA-MLIDS model in the ID process.

The prediction of ship motion attitude in seaway based on BSO-VMD-GRU combination model

Short-term prediction of ship attitude holds the potential to provide decision support for precise control and optimal timing selection of ship operations at sea, thereby ensuring the safety and efficiency of offshore activities. However, the time series data representing ship motion attitude exhibits complex nonlinear and non-stationary characteristics, which pose challenges to achieving accurate predictions. To improve prediction accuracy, this study employs two sequence decomposition techniques: Variational Mode Decomposition (VMD) and Adaptive Noise Complete Ensemble Empirical Mode Decomposition (CEEMDAN). These decomposition methods are integrated with a Gated Recurrent Unit (GRU) to forecast ship motion attitude. Firstly, the decomposition techniques are applied to decompose the time series data of ship motion attitude into multiple intrinsic mode function (IMF) components. Subsequently, each IMF component is predicted using the GRU. Ultimately, the final prediction results are obtained by aggregating the GRU’s predictions for each individual component. To alleviate the impact of manual parameter configuration in the VMD algorithm, this study introduces a metaheuristic optimization algorithm called the Binary System Optimization Algorithm (BSO), which is empirically validated for its effectiveness. Moreover, real ship data from the vessel named “Yukun” is utilized to experimentally verify the proposed prediction algorithm. The experimental outcomes reveal that both decomposition algorithms can enhance the prediction accuracy of the GRU, with the VMD algorithm yielding superior prediction results compared to those obtained using CEEMDAN.

A novel binary Kepler optimization algorithm for 0–1 knapsack problems: Methods and applications

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned tometaheuristic algorithmsfor their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts theKepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create abinary variantcalled BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to severalcompeting optimizerswhen solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for thegenetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA,hybrid binary manta ray foraging optimization, andhybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions.

Binary Semantic Pattern Rules for Chinese-English Machine Translation Based on Machine Learning Algorithms

With the increase of internationalization and the exponential growth of intercultural communication, the importance of interlingual translation has become increasingly prominent. Machine translation has been a booming area of research as technology has advanced. Due to the complexity of language ability and limited understanding of language laws, there are challenges for machine translation. This paper focused on how to construct and apply binary semantic pattern rules through machine learning to improve the translation effect in Chinese-English machine translation. The research results of this paper would contribute to the further development and improvement of Chinese-English machine translation technology. In order to produce high-quality translation results, research in machine translation has recognized the need to analyze and understand the semantics of natural language. To address the important issue of lexical and syntactic ambiguity, representations of binary semantic pattern rules have been developed to formally describe these rules. Based on this, this paper designed and implemented a corpus-based binary semantic rule extraction and optimization algorithm, which used machine learning algorithms to automatically detect the semantic rules of two or more than two phrases in the Chinese corpus, and then automatically optimized and converted them according to the statistical results, and realized the design of Chinese-English machine translation system. The article evaluated the quality of machine translation to test the effectiveness of machine translation binary semantic pattern rules based on machine learning algorithms. The study found that compared with the rule set A, the rule sets B and C obtained automatically by the rule mining algorithm had significantly improved accuracy, both reaching more than 90%. This showed that the binary semantic pattern rule mining algorithm and optimization algorithm proposed in this paper were reasonable.

Deep residual convolutional neural Network: An efficient technique for intrusion detection system

The fast growth of computer networks over the past few years has made network security in smart cities a significant issue. Network intrusion detection is crucial to maintaining the integrity, confidentiality, and resource accessibility of the various network security rules. Conventional intrusion detection systems frequently use mining association rules to identify intrusion behaviors. They run into issues such as a high false alarm rate (FAR), limited generalization capacity, and slow timeliness because they cannot adequately extract distinctive information about user activities. The primary goal of the current research is to classify attacks using efficient approaches to identify genuine packets. If the number of characteristics in a dataset decreases, the complexity of DL approaches is significantly decreased. In this research work, the Deep Residual Convolutional neural network (DCRNN) is proposed to enhance network security through intrusion detection, which is optimized by the Improved Gazelle Optimization Algorithm (IGOA). Feature selection has eliminated irrelevant features from network data used in obstacle classification processes. Essential features are chosen using the Novel Binary Grasshopper Optimization Algorithm (NBGOA). Experimentation is carried out using the UNSW-NB-15, Cicddos2019 dataset, and CIC-IDS-2017 dataset. According to the experimental findings, the proposed system outperforms existing models regarding classification accuracy and processing time. The results demonstrate that the presented approach efficiently and precisely identifies various assaults.

Cluster head selection strategy of WSN based on binary multi-objective adaptive fish migration optimization algorithm

In deployed wireless sensor networks (WSNs), how to efficiently transmit information collected by sensor nodes with limited energy is a challenging problem. An appropriate cluster head selection strategy can efficiently solve this problem, but there are many factors to be considered, such as energy consumption, the coverage of cluster head nodes, and the number of cluster head nodes. Each factor has a profound impact on the performance of wireless sensor networks, and there are conflicts among them. In order to solve the conflict of multiple factors and obtain the optimal selection strategy of the cluster head node, this paper proposes a Binary Multi-Objective Adaptive Fish Migration Optimization (BMAFMO) algorithm. The algorithm introduces the Pareto optimal solution storage strategy to improve the global search ability of the optimization algorithm and transform the continuous solution into a binary solution according to the sigmoid transformation function to solve the problem of cluster head node selection. The new algorithm was comprehensively tested using eight test problems and four test metrics. At the same time, the reliability of the algorithm is tested by rank sum test. The test results show that the BMAFMO algorithm obtained the best results in 78.13% test problems compared with other algorithms. Finally, the BAMFMO algorithm is applied to solve the cluster head selection problem of WSN and the simulation results show the novel algorithm has better optimization ability than other heuristic algorithms.