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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Mohamed, Rozlini Samsuddin, Noor Azah Yusof, Munirah Mohd Univ Tun Hussein Onn Malaysia Batu Pahat 86400 Kagawa Malaysia

ISBN: (纸本)9783031669644;9783031669651

This study focuses on predicting sound categories in music datasets, employing classification to match predicted sounds with correct instrument categories. Sound classification in music datasets is challenging due to factors such as sampling and digital representation. Discretization at a sufficiently high rate helps preserve the original signal than ensures that classify all relevant information to appropriate group. This study proposes a novel approach that integrates K-Means clustering and the bat algorithm for efficient data discretization, aiming to improve sound category prediction accuracy. Unlike existing methods, our approach optimizes the data split points, leading to more informative and well-separated groups for classification. We tested our method with two classifiers, Naive Bayes and K-Nearest Neighbors, and observed significant improvements in classification performance compared to using raw data or other discretization methods. This approach offers a promising way to enhance the accuracy of sound prediction in music applications.

关键词： bat algorithm Discretization Prediction K-Means

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An Optimized Neural Network with bat algorithm for DNA Sequence Classification

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Computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedbat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed batLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as bat levy flight Artificial NeuralNetwork (batLFANN) and bat levy Flight Back Propagation (batLFBP) arecompared with the other state-of-the-art algorithms like bat Artificial NeuralNetwork (batANN), bat back propagation (batBP), bat Gaussian distribution Artificial Neural Network (batGDANN). And bat Gaussian distributionback propagation (batGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed batLFANN achieved 99.88153% accuracy with MSE of 0.001185,and batLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed batLFANN achieved 99.89899% accuracy withMSE of 0.00101, and batLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed batLFANN achieved 99.82853%accuracy with MSE of 0.001715, and batLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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An improved bat algorithm with velocity weight and curve decreasing

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JOURNAL OF SUPERCOMPUTING 2022年第10期78卷 12461-12475页

作者： Ye, Yun Zhao, Xiaojuan Xiong, Lu Guangdong Ind Polytech Guangzhou 510300 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510070 Guangdong Peoples R China

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization, which has fast convergence, a simple structure, and strong search ability. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this problem, the paper proposes a hybrid approach to improving its local search mechanism. The local search strategy of curve decreasing and speed weight is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. The performance of the improved bat algorithm has better global optimization ability and higher convergence accuracy than the standard bat algorithm.

关键词： Clustering algorithm bat algorithm Curve decreasing strategy Speed weight

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bat4CEP: a bat algorithm for mining of complex event processing rules

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APPLIED INTELLIGENCE 2022年第13期52卷 15143-15163页

作者： Bruns, Ralf Dunkel, Juergen Hannover Univ Appl Sci & Arts Dept Comp Sci Ricklinger Stadtweg 120 D-30459 Hannover Germany

Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel bat-inspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.

关键词： bat algorithm Swarm algorithm Rule learning Complex event processing

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Optimizing Sheep Breed Classification with bat algorithm-Tuned CNN Hyperparameters

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SN Computer Science 2024年第2期5卷 219页

作者： Ravikiran, H.K. Jayanth, J. Sathisha, M.S. Bindu, K. Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering GSSSIETW Karnataka Mysuru 570016 India Department of Computer Science and Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering Global Academy of Technology Karnataka Bengaluru 560098 India

Efficiently classifying sheep breeds through image analysis is pivotal in modern animal husbandry, influencing critical management and breeding decisions. This study delves into automating this process by harnessing Convolutional Neural Networks (CNNs), with a particular focus on optimizing key hyperparameters—the learning rate and dropout rate—essential for refining model performance. Manual hyperparameter tuning is often time-consuming and demands expertise. To overcome this challenge, we introduce an innovative approach that utilises the bat algorithm, a bio-inspired optimization technique. This algorithm mimics bat echolocation behaviors, skillfully navigating the complex hyperparameter search space to determine optimal values. By dynamically adjusting CNN hyperparameters, our research aims to boost classification accuracy while simplifying the tuning process. Empirical results highlight significant gains in classification accuracy and emphasizes the bat algorithm's efficacy. The optimized CNN model, empowered by fine-tuned hyperparameters, demonstrates superior performance, promising practical applicability in real-world sheep breed classification scenarios. This study meticulously adjusts pulse rate and loudness, revealing an optimal combination of [0.001, 0.24404868], which substantially improves model performance. The findings emphasize the bat algorithm's role in streamlining hyperparameter tuning and its potential impact on automated sheep breed classification. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： bat algorithm Classification CNN Dropout Hyperparameter tuning Learning rate

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Modified bat algorithm for Optimal VM’s in Cloud Computing

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Computers, Materials & Continua 2022年第8期72卷 2877-2894页

作者： Amit Sundas Sumit Badotra Youseef Alotaibi Saleh Alghamdi Osamah Ibrahim Khalaf Department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Al-Nahrain Nano-Renewable Energy Research Center Al-Nahrain UniversityBaghdad10072Iraq

All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are *** purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load *** cloud computing environment is the subject of this *** consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s *** case study is one of the most important segments of cloud computing,i.e.,Load *** paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing *** proposed method led to the proposal and implementation of an algorithm inspired by the bat algorithm(BA).This proposed Modified bat algorithm(MBA)allows balancing the load among virtual *** proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified bat algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with bat algorithm.

关键词： bat algorithm cloud computing fitness value calculation load balancing modified bat algorithm

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Improvements of bat algorithm for optimal feature selection: A systematic literature review

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INTELLIGENT DATA ANALYSIS 2022年第1期26卷 5-31页

作者： Al-Dyani, Wafa Zubair Ahmad, Farzana Kabir Kamaruddin, Siti Sakira Univ Utara Malaysia Coll Arts & Sci Sch Comp Sintok 06010 Malaysia Hadhramout Univ Coll Comp & Informat Technol Dept Comp Sci Hadhramout Yemen

bat algorithm (BA) has been extensively applied as an optimal Feature Selection (FS) technique for solving a wide variety of optimization problems due to its impressive characteristics compared to other swarm intelligence methods. Nevertheless, BA still suffers from several problems such as poor exploration search, falling into local optima, and has many parameters that need to be controlled appropriately. Consequently, many researchers have proposed different techniques to handle such problems. However, there is a lack of systematic review on BA which could shed light on its variants. In the literature, several review papers have been reported, however, such studies were neither systematic nor comprehensive enough. Most studies did not report specifically which components of BA was modified. The range of improvements made to the BA varies, which often difficult for any enhancement to be accomplished if not properly addressed. Given such limitations, this study aims to review and analyse the recent variants of latest improvements in BA for optimal feature selection. The study has employed a standard systematic literature review method on four scientific databases namely, IEEE Xplore, ACM, Springer, and Science Direct. As a result, 147 research publications over the last ten years have been collected, investigated, and summarized. Several critical and significant findings based on the literature reviewed were reported in this paper which can be used as a guideline for the scientists in the future to do further research.

关键词： bat algorithm feature selection bat improvements systematic review

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A novel upgraded bat algorithm based on cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems

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ENGINEERING WITH COMPUTERS 2022年第2期38卷 1731-1758页

作者： Pathak, Vimal Kumar Srivastava, Ashish Kumar Manipal Univ Jaipur Dept Mech Engn Jaipur 303007 Rajasthan India

The bat algorithm (BA) is one of prominent swarm-based algorithm that has the suitability in solving only small dimension engineering problems and suffers from drawback of getting trapped in local minimum with slow convergence for multi-dimensional problems. In the context of improving its applicability in solving large scale and constrained engineering design problems, this paper presents a novel upgraded bat algorithm with cuckoo search and Sugeno inertia weight (UBCSIW). In the proposed UBCSIW algorithm, first, the bat algorithm with its competence to exploit the optimal solutions in search space is combined with cuckoo search with its ability to explore best solution globally using Levy flight in the search space. Secondly, a new velocity and position search equation is incorporated in which the bat searches around the best candidate solution. This step helps in establishing adequate balance between exploration and exploitation capability and improving the performance effectively by employing greedy selection to choose the best candidate solution. Finally, Sugeno fuzzy inertia weight is introduced in the velocity updation equation, boosting the flexibility and diversity of bat population and results in stability of results. The effectiveness of the proposed UBCSIW algorithm is tested on 16 standard benchmark functions (unimodal and multimodal) with different dimensions, 12 CEC2015 test functions and 7 well-known constrained engineering design problems. The outputs of the proposed UBCSIW algorithm are validated by comparison with classical BA and other swarm-based state-of-the art algorithms. The simulation results show that proposed UBCSIW algorithm achieves highly competitive results in terms of higher optimization accuracy and improved convergence that outperforms basic BA in all twenty-eight test functions while performs better than other competitive algorithms in 24 functions (13 benchmark and 11 CEC2015 functions).

关键词： bat algorithm Swarm-based algorithms Constrained optimization Large scale problems

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Wind integrated power system to reduce emission: An application of bat algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第2期42卷 1041-1049页

作者： Rao, B. Venkateswara Devarapalli, Ramesh Malik, Hasmat Bali, Sravana Kumar Garcia Marquez, Fausto Pedro Chiranjeevi, Tirumalasetty VR Siddhartha Engn Coll Dept EEE Vijayawada India BIT Sindri Dept EE Dhanbad Jharkhand India IIT ISM Dept EE Dhanbad Jharkhand India BEARS NUS Campus Singapore University Town Singapore GITAM Deemed Univ Dept EEE Visakhapatnam Andhra Pradesh India Univ Castilla La Mancha Ingenium Res Grp Ciudad Real Spain Rajkiya Engn Coll Sonbhadra Dept EE Churk Uttar Pradesh India

The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired bat algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved.

关键词： bat algorithm emission optimal power flow SVC wind power

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Variants of bat algorithm for solving partitional clustering problems

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 3期38卷 1973-1999页

作者： Kumar, Yugal Kaur, Arvinder Jaypee Univ Informat Technol Dept Comp Sci & Engn Solan Himachal Prades India

Clustering is an exploratory data analysis technique that organize the data objects into clusters with optimal distance efficacy. In this work, a bat algorithm is considered to obtain optimal set of clusters. The bat algorithm is based on the echolocation feature of micro bats. Moreover, some improvements are proposed to overcome the shortcoming associated with bat algorithm like local optima, slow convergence, initial seed points and trade-off between local and global search mechanisms etc. An enhanced cooperative co-evolution method is proposed for addressing the initial seed points selection issue. The local optima issue is handled through neighbourhood search-based mechanism. The trade-off issue among local and global searches of bat algorithm is addressed through a modified elitist strategy. On the basis of aforementioned improvements, three variants (BA-C, BA-CN and BA-CNE) of bat algorithm is developed and efficacy of these variants is tested over twelve benchmark clustering datasets suing intra-cluster distance, accuracy and rand index parameters. Simulation results showed that BA-CNE variant achieves more effective clustering results as compared to BA-C, BA-CN and BA. The simulation results of BA-CNE are also compared with several existing clustering algorithms and two statistical tests are also applied to investigate the statistical difference among BA-CNE and other clustering algorithms. The simulation and statistical results confirmed that BA-CNE is an effective and robust algorithm for handling partitional clustering problems.

关键词： Echolocation Neighbourhood Cooperative co-evolution Elitist strategy Clustering bat algorithm

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