Hybrid Bat Algorithm Research Articles

An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients.

Appendicitis, an infection and inflammation of the appendix is a prevalent condition in children that requires immediate treatment. Rupture of the appendix may lead to several complications, such as peritonitis and sepsis. Appendicitis is medically diagnosed using urine, blood, and imaging tests. In recent times, Artificial Intelligence and machine learning have been a boon for medicine. Hence, several supervised learning techniques have been utilized in this research to diagnose appendicitis in pediatric patients. Six heterogeneous searching techniques have been used to perform hyperparameter tuning and optimize predictions. These are Bayesian Optimization, Hybrid Bat Algorithm, Hybrid Self-adaptive Bat Algorithm, Firefly Algorithm, Grid Search, and Randomized Search. Further, nine classification metrics were utilized in this study. The Hybrid Bat Algorithm technique performed the best among the above algorithms, with an accuracy of 94% for the customized APPSTACK model. Five explainable artificial intelligence techniques have been tested to interpret the results made by the classifiers. According to the explainers, length of stay, means vermiform appendix detected on ultrasonography, white blood cells, and appendix diameter were the most crucial markers in detecting appendicitis. The proposed system can be used in hospitals for an early/quick diagnosis and to validate the results obtained by other diagnostic modalities.

Research on an identification model for mine water inrush sources based on the HBA-CatBoost algorithm

Accurate and efficient identification of water inrush sources, as one of the three critical elements of mine water hazards, is crucial for mine water management. To identify the sources of mine water inrush effectively, a model named HBA-CatBoost is introduced. This model is established on the hybrid bat algorithm (HBA)-optimized category feature gradient boosting tree (CatBoost), and the shapley additive explanation (SHAP) method is employed to elucidate the model’s decision-making process. Given the prevalent occurrence of water hazards in coal seam roofs and floors in the northern Guizhou coalfield, coupled with the challenges in pinpointing water inrush sources in mines, the HBA-CatBoost model is tested at the Longfeng Coal Mine in northern Guizhou to validate its practicality. Comparative analysis with the HBA-RF, HBA-XGBoost, CatBoost, RF, and XGBoost models demonstrates that the hybrid bat algorithm significantly enhances the classification performance of the CatBoost model, resulting in improved convergence speed and classification accuracy. The HBA-CatBoost model outperforms the aforementioned models in terms of classification effectiveness, achieving accuracy, recall, precision, and F1 scores of 96.43%, 97.22%, 96.43%, and 96.61%, respectively. The SHAP method elucidates the decision mechanism of the optimal HBA-CatBoost model, highlighting the significance of sample features and bolstering the model’s credibility. These outcomes underscore the superior performance of the HBA-CatBoost model and its potential for effectively identifying water inrush sources in mines.

Conceptual design and optimization of integrating renewable energy sources with hydrogen energy storage capabilities

Systems are inherently tied to uncertainty, which necessitates the development of designs and schedules that accommodate this unpredictability. Particularly in modern power systems, the variable efficiency of numerous factors, including pricing, underscores the need for uncertainty modeling. In the meantime, the incorporation of EVs (electric vehicles) into the electrical grid is advancing, with these vehicles being recognized for their role in reducing dependency on fossil energies and enhancing the resilience, stability and efficiency of the grid. However, the rapid growth of the EV sector necessitates thoughtful strategies from decision-makers to manage its expansion effectively, particularly considering the environmental implications associated with the life cycle of EVs, including their charging and discharging processes. This research introduces the conceptual design and optimization approach utilizing a hybrid bat algorithm and differential evolution algorithm (BA-DEA) to improve the efficiency and resilience of EV-smart parking lots under the unpredictable conditions caused by grid pricing fluctuations within the DRP (demand response program). The technique effectively modulates daily costs by adjusting loads among peak and off-peak periods. Key features of the proposed approach include a non-dominated sorting model, innovative variable identification, a memory-based selection process, and the application of fuzzy theory to identify optimal Pareto outcomes. This methodology is not only swift in converging to a solution but also demonstrates a high likelihood of reaching the global optimum. Modeling considerations for smart hydrogen storage systems (SHSSs) incorporate significant constraints, notably those associated with electrolyzers, fuel cells, and storage capacities. The algorithm's effectiveness is validated in scenarios involving parking facilities and multiple uncertain resources, demonstrating a robust reduction in specific cost indicators and adjustments in cost dynamics when DRP is considered. Specifically, the algorithm achieves a substantial 42% decrease in cost variability when DRP is excluded, and a 46.9% reduction in cost variability when DRP is included, despite a minor 5% increase in average cost. These outcomes underscore the proposed system's capability to improve resilience and thermal performance under fluctuating conditions, making it a promising solution for future smart energy systems. Further, the results exhibited the complex interplay between cost optimization and operational adjustments in response to demand-side management.

Hybrid Bat and Salp Swarm Algorithm for Feature Selection and Classification of Crisis-Related Tweets in Social Networks

Hybrid Bat and Salp Swarm Algorithm for Feature Selection and Classification of Crisis-Related Tweets in Social Networks

Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process

Rate of penetration (ROP) accurate prediction can effectively reduce drilling cycle time and operation cost. However, the complexity and variability of drilling process is not conducive to accurate ROP modeling. The existing ROP models focus on static prediction considering drilling data, which cannot effectively solve the ROP mutation problem caused by complex formations. Some ROP online prediction studies have great real-time capture capability, but without considering the formation lithology adequately, and can be limited in practical application. In this paper, a multi-source information fusion-based dynamic model for online prediction of ROP in drilling process is proposed, which consists of three stages (drilling data pre-processing, optimized ROP modeling, and ROP model updating). In the first stage, three drilling parameters including weight on bit, rotational speed, and torque are filtered by using the outlier removing and wavelet filtering techniques. In the second stage, a novel ROP modeling method, named as hybrid bat algorithm optimized - restricted Boltzmann machine - back propagation neural network (HBAO-RBM-BPNN) is proposed to build the ROP prediction model. In the last stage, formation drillability variation and time interval are used as update conditions, and the established ROP model is updated via a moving window strategy to realize high accuracy online prediction of ROP. Multi-source (formation and drilling) information fusion is considered in this key stage. Finally, the proposed method and seven ROP prediction methods (two offline and five online) are compared through the use of industrial data from a drilling site in Dandong area, Northeast China. The comparison results verify the effectiveness of the proposed method, which lays a foundation for intelligent optimization control of complex geological drilling process.

MOHBA:multi-objective workflow scheduling in cloud computing using hybrid BAT algorithm

MOHBA:multi-objective workflow scheduling in cloud computing using hybrid BAT algorithm

A novel optimization method for geological drilling vertical well

Geological drilling is an important means for exploration of the earth. Due to the nonlinearity and coupling, geological drilling is accompanied by low efficiency and safety, and difficult to accurately predict the drilling states. Moreover, the vertical well trajectory needs to follow the plumb line. A novel optimization method is proposed to solve these problems. First, working conditions are identified by fuzzy C-means clustering method and corresponding adjustment ranges of operating variables are refined for optimization. Meanwhile, support vector regression and hybrid bat algorithm are employed to construct reliable models for mud pit volume (MPV) and rate of penetration (ROP). Then, how to improve safety and efficiency is converted to a two-objective problem, that is to suppress MPV fluctuations and improve ROP with vertical well constraints. Nondominated sorting genetic algorithm II is invoked to solve this problem, and the appropriate solution is selected by two references points. Moreover, a short-time scale and long-time scale optimization strategy is introduced to cope with different adjustment interval of operating variables. Finally, the simulation results based on actual drilling data, application results in a semi-physical experiment system and comparison results from a vertical well verify the effectiveness and practicability for the developed method.

A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch

Large-scale grids have gradually become the dominant trend in power systems, which has increased the importance of solving the challenges associated with large-scale economic dispatch (LED). An increase in the number of decision variables enlarges the search-space scale in LED. In addition to increasing the difficulty of solving algorithms, huge amounts of computing resources are consumed. To overcome this problem, we proposed a surrogate-assisted adaptive bat algorithm (GARCBA). On the one hand, to reduce the execution time of LED problems, we proposed a generalized regression neural network surrogate model based on a self-adaptive “minimizing the predictor” sampling strategy, which replaces the original fuel cost functions with a shorter computing time. On the other hand, we also proposed an improved hybrid bat algorithm (RCBA) named GARCBA to execute LED optimization problems. Specifically, we developed an evolutionary state evaluation (ESE) method to increase the performance of the original RCBA. Moreover, we introduced the ESE to analyze the population distribution, fitness, and effective radius of the random black hole in the original RCBA. We achieved a substantial improvement in computational time, accuracy, and convergence when using the GARCBA to solve LED problems, and we demonstrated this method’s effectiveness with three sets of simulations.

Insight into breast cancer detection: new hybrid feature selection method

Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03.

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

A Mobile Ad Hoc Networks (MANETs) comprises of the vast range of devices such as sensors, smart phones, laptops and other mobile devices that connect with each other across wireless networks and collaborate in a dispersed fashion to offer network functions in the absence of a permanent infrastructure. The Cluster Head (CH) selection in a clustered MANET is still crucial for lowering each node's energy consumption and increasing the network's lifetime. However, in existing clustering mechanism trust of the all nodes are presumed those causes increased challenge in the MANET environment. Security is a crucial factor when constructing ad-hoc networks. In a MANET, energy consumption in route optimization is dependent on network resilience and connectivity. The primary objective of this study is to design a reliable clustering mechanism for MANETs that takes energy efficiency into account. For trusted energy-efficient CH in the nodes, a safe clustering strategy integrating energy-efficient and fuzzy logic based energy clustering is proposed to address security problems brought about by malicious nodes and to pick a trustworthy node as CH. To improve the problem findings Bat algorithm (BAT) is integrated with Particle Swarm Optimization (PSO). The PSO technique is inspired because it imitates the sociological characteristics of the flock of the birds through random population. The BAT is a metaheuristic algorithm inspired by microbat echolocation behavior that uses pulse average with global optimization of the average path in the network. Hybrid Particle Swarm Optimization (HPSO) and BAT techniques are applied to identify the best route between the source and destination. According to the simulation results, the suggested Fuzzy logic Particle Swarm Optimization BAT (FLPSO-BAT) technique has a minimum latency of 0.0019 milliseconds, with energy consumption value of 0.09 millijoules, maximal throughput of 0.76 bits per sec and detection rate of 90.5% without packet dropping attack.

Fuzzy based hybrid BAT and firefly algorithm for optimal path selection and security in wireless sensor network

Nodes are deployed randomly in the network area of the WSN. data transmission from source to destination via intermediate nodes should be done in a secure fashion. Due to the large size of packet loss and energy consumption of sensor nodes, a secure and energy-efficient path must be required. The main objective of this research is to provide secure data transmission among node-to-node for efficient delivery of data packets to the destination. The system uses a novel hybrid firefly and BAT algorithm for path selection, an innovative trust value generation, and optimal neighborhood selection using fuzzy logic. The research employs Elliptic Curve Cryptography (ECC) combined with Diffie-Hellman exchange for key generation and key exchange. Path selection is done by fuzzy logic and optimization of selection has been carried out by hybrid BAT and Firefly algorithms. Key generation includes a time-based randomness factor that increases the complexity of cryptanalysis, thereby providing the most security. The performance of the simulation is analyzed and depicted in terms of delay, throughput, energy, and processing time. The research has been carried out using a network simulator with nodes deployed randomly in the network area with mobility as the primary concern that requires dynamic path selection.

The multi-product vehicle routing problem with cross-docking: a novel strategy hybrid bat algorithm for Industry 3.5 in Thailand's food industry

ABSTRACT This paper presents a multi-product vehicle routing problem with cross-docking operations for food industry to ensure products which can be delivered in time and with minimum transportation and hiring costs to perform for both supplier side and customer side. The problem was formulated as a special multi-product vehicle routing problem with cross-docking and time window constraints (MPVRPCDTW). A novel strategy hybrid based on the Bat algorithm (NSHBA) is presented to solve the MPVRPCDTW for the sustainable food industry by minimising the transportation cost and the number of vehicles, using the poultry industry as a case study. Additionally, a new disturbance algorithm (DA) was developed to find the best neighbourhood strategy to increase the efficiency and quality of solutions in the NSHBA. The computational results revealed that the NSHBA-DA outperformed the Bat algorithm, the differential evolution algorithm, and the particle swarm optimisation algorithm. The MATLAB App architecture based on all eight algorithms was designed and developed. The overall result of this study should prove to benefit the logistics staff in making the right decisions on cross-docking transportation by employing real-time data.

Multipath routing protocol based on backward routing with optimal fuzzy logic in medical telemetry systems for physiological data measurements

<p class="Abstract">Mobile ad hoc network (MANET) performance is critically affected by the mobility and resource constraints of nodes. Node mobility will impact on connecting stability, and node resource limitations will result in congestion, so the development of a routing protocol that promotes quality of service (QoS) in MANET is quite difficult. In particular, frequent interrupting connection may degrade QoS performance in the high-speed node drive scenario, so it is required to build the MANET protocol routing which can be adapted for changes in the networking architecture to support QoS. Moreover, MANET's multipath routing is the most necessary for secure transmission that can be achieved if self-centered nodes in the MANET network are ignored. Secure routing guarantees the reliability of data, confidentiality, authorization and authentication and non-denial. A novel safe multi-way method for dependable data transfer, which depends on the quality of service, is offered in this study. The Ad Hoc on Demand Backward Routing protocol with Optimal Fuzzy Logic (OFL) is also designed to provide multi-path routing. The hybridization of the bat optimization approach provides the optimum route by generating rules in OFL, and hence chooses an optimal rule. The efficiency of the planned technique is evaluated for factors such as final delay, the packet delivery ratio and so on. When the node is 150, the proposed hybrid bat algorithm achieved 0.61 of average packet latency, where the existing technique Particle Swarm Optimization achieved nearly 1.2 to 1.3 packet latency. In this communication network, measurement of packet delivery ratio is an important task for establishing the communication network. The results show that our suggested work has more energy efficiency and network lifetime than existing ones.</p>

A novel modeling and drilling optimization method with suitable constraints in geological well

Drilling is of great importance in developing resources. Rate of penetration (ROP) and mud pit volume (MPV), which reflect drilling efficiency and safety, need to be predicted accurately and maintained in appropriate ranges. Due to the complexity and coupling in the drilling process, drilling safety and efficiency are difficult to improve simultaneously and reliable nonlinear prediction models also need to be constructed. Moreover, the values of optimization objectives are needed to manipulated in proper ranges. This paper presents a modeling and optimization method to solve these problems. In the modeling part, a novel method based on hybrid bat algorithm (HBA) and support vector regression (SVR) is developed to build prediction models, one for predicting ROP and the other for MPV. Then, the objectives intervals are obtained by the fuzzy comprehensive evaluation method, which analyzes the influences of multiple variables. Finally, a novel drilling optimization method is developed to improve ROP and reduce MPV fluctuations according to the objectives intervals and nondominated sorting genetic algorithm II (NSGA-II). The simulation results indicate that prediction models constructed by the developed method have superior performance than other modeling methods. Moreover, the developed method can improve ROP by an average of 35.8% and reduce the fluctuations of the MPV by an average of 34.3%, which means the developed method can improve safety and efficiency at the same time. The developed method is also applied in drilling equipment, the running results further illustrate the validity.

A Hybridized Bat Algorithm for effective Task

The recently developed distributed framework Cloud Computing (CC) offers versatile and dynamically scalable low-cost computing services. In CC environment Task Scheduling (TS) is the key issues to be tackled to boost system performance and increase cloud satisfaction for customers. Advanced programming techniques should be introduced to ensure an efficient cloud mapping of tasks to fulfil the complex necessities of end-users applications. Having regard to a timing problem, unrelated MTs are responsible to perform individual tasks, is the key motivation behind this study. There are many subtasks to a mission. Only after completion of its predecessor subtask can a successor subtask be started. The subtasks may be carried out in the same machine or in another machine independently. It is known in advance how long each machine is run. Due to the continuous processing of other work, each computer has known release time (i.e., the time available to perform current tasks). We also considered two variants of problems for two separate objective functions in this research paper. The first version of the problem takes note of minimising the overall finishing time target while the second version considers minimising the Making-Time goal. In this paper we proposed an algorithm for hybridised bat optimization for this multi-target TS. Unlike current algorithms, an optimum solution can be sought by using fewer iterations. We have compared to determine the performance of this enhanced Bat Algorithm with the existing systems. The experiment conclude that the proposed procedureacts well in comparison with other TS algorithms.

Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm

Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain.

A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem

In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the population diversity by classifying the populations, which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat’s velocity and location, solving the difficulty of the algorithm to trade-off exploration and mining capacity. Finally, the Gaussian learning strategy and elite learning strategy assist the whole population to jump out of the local optimal frontier. The simulation results demonstrate that the algorithm proposed in this paper can well solve the DAPFSP. In addition, compared with other metaheuristic algorithms, IHBA has better performance and gives full play to its advantage of finding optimal solutions.

Urban expansion simulation and development-oriented zoning of rapidly urbanising areas: A case study of Hangzhou

Sustainable urban development is the key to regional urban development policy-making. Therefore, the comprehensive spatial zoning of rapidly urbanising areas is important. In this study, a novel spatial zoning framework was established based on the future urban spatiotemporal pattern and multidimensional dynamic index system at the township scale. First, the urban expansion of Hangzhou in 2025 was simulated based on a new method in which the hybrid bat algorithm and deep belief network (DBN) are coupled with the cellular automata (CA) model (MDBN-CA). Second, an urban development-oriented evaluation system was established at the township scale based on urban expansion simulations and indicators, including the speed and intensity, morphology, socioeconomic and ecological benefits. Finally, Hangzhou was zoned by using the K-means method. The results show that: (1) The MDBN-CA model effectively overcomes the limitations of traditional neural networks, yielding an increase in the simulation accuracy and spatial pattern similarity of 3.70% and 10.11%, respectively; (2) Hangzhou can be divided into six zones according to the 2025 urban expansion, that is, the highly urbanised, key urbanised, radiation, potential, optimised, and ecological priority zones; (3) Based on the current development trends, urban expansion in Hangzhou will have relatively large benefits by 2025. However, problems with respect to the unbalanced development of land urbanisation and population urbanisation, as well as the low efficiency of land use, were identified. Based on the results of this study, suggestions are provided with respect to spatial pattern reconstruction, urban function transformation, efficient land use, and green and healthy development; (4) Based on the combination of the MDBN-CA and K-means models with the zoning framework of the comprehensive benefits evaluation system of urban expansion, future urban expansion can be predicted and analysed.

A Self-Adaptive Hybrid Bat Algorithm for Training Feedforward Neural Networks

Training feedforward neural network (FFNN) is a complex task in the supervised learning field. An FFNN trainer aims to find the best set of weights that minimizes classification error. This paper presents a new training method based on hybrid bat optimization with self-adaptive differential evolution to train the feedforward neural networks. The hybrid training algorithm combines bat and the self-adaptive differential evolution algorithm called BAT-SDE. BAT-SDE is used to better search in the solution space, which proves its effectiveness in large space solutions. The performance of the proposed approach was compared with eight evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using seven bio-medical datasets and one large credit card fraud detection dataset. The results of the comparative study show that BAT-SDE outperformed other training methods in most datasets and can be an alternative to other training methods.

Collaborative optimization of dynamic grid dispatch with wind power

Economic dispatch can promote the economic operation of power systems, and reactive power dispatch can ensure the safe and reliable operation of power systems. Essentially different from the related literature with separately optimizing the two dispatches, this paper proposes the collaborative optimization strategy of dynamic grid dispatch to optimize economic dispatch and reactive power dispatch simultaneously. The strategy is devoted to analyzing the internal relationship of the two dispatches, and establishing a collaborative optimization model to complete the collaborative optimization of the two dispatches. Moreover, multi-objective hybrid bat algorithm is improved by an unbalanced power distribution method, which is suitable to solve dynamic grid dispatch problem. Finally, wind power predicted by neural network is integrated in the collaborative optimization of dynamic grid dispatch, and the influence of wind power integration on grid dispatch is studied. The numerical examples on the IEEE 30-bus are provided to demonstrate the superiority of the proposed strategy.