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A novel path planning method of mobile robots based on an improved bat algorithm

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第16期235卷 3071-3086页

作者： Wang, Zhongbin Wu, Ziqing Si, Lei Tong, Kuangwei Tan, Chao China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Jiangsu Engn Technol Res Ctr Intelligent Equipmen Xuzhou Jiangsu Peoples R China

In order to solve the global path planning problem of mobile robots, an improved bat algorithm based on inertial weight and Levy flight is proposed in this paper. The linear inertial weights are used to prevent the algorithm from converging prematurely and the Levy flight is introduced in the global search stage to change the flight direction of the bat individuals. Furthermore, in the local search stage, the random exploration mechanism in Cauchy Distribution is utilized to enhance the local mining ability of the algorithm and search for the local optimal values. Then, some simulations are provided to verify the superiority of the improved bat algorithm to other optimization algorithms. Finally, the improved bat algorithm is applied in the global path planning, and the environment model and fitness function construction are reasonably established. The results indicate the feasibility and effectiveness of proposed algorithm in solving path planning problems.

关键词： bat algorithm path planning Levy flight Cauchy distribution adjustment of inertia weight

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Comparative analysis of various compensating devices in energy trading radial distribution system for voltage regulation and loss mitigation using Blockchain technology and bat algorithm

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ENERGY REPORTS 2021年 7卷 8312-8321页

作者： Yuvaraj, T. Devabalaji, K. R. Srinivasan, S. Prabaharan, Natarajan Hariharan, R. Alhelou, Hassan Haes Ashokkumar, B. Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Elect Engn Chennai Tamil Nadu India Hindustan Inst Technol & Sci Dept Elect & Elect Engn 1 OMR Rd Chennai Tamil Nadu India Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biomed Engn Chennai Tamil Nadu India SASTRA Univ Sch Elect & Elect Engn Dept Elect & Elect Engn Thanjavur India Univ Coll Dublin Sch Elect & Elect Engn Dublin 4 Ireland Thiagarajar Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India

The electric power distribution plays a crucial part in the power systems to maintain the power quality and stability of the system for loss mitigation and voltage regulation. The reactive power compensators like a capacitor, DG, and DSTATCOM play a vital role in power quality and stability improvement by providing proper reactive power in the distribution system. This article provides a critical review and comparative analysis of various compensating devices allocation to achieve power loss mitigation and voltage regulation in the radial distribution system (RDS). The bat algorithm (BA) and Voltage Stability Index (VSI) are utilized for determining the optimal size and site of the compensating devices in the RDS. Further, the application of Blockchain technology is utilized for voltage regulation of energy trading RDS. To show the effectiveness of the present study, an IEEE 33 test system is considered. All three compensators are implemented in IEEE 33 test system and compared with existing approaches. The present study is very much helpful to the Distribution Network Operators for selecting the suitable compensator in real-time applications. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： DG Capacitor DSTATCOM bat algorithm Blockchain technology Power loss mitigation Voltage regulation Energy trading Radial distribution system

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Feature selection in intrusion detection systems: a new hybrid fusion of bat algorithm and Residue Number System

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JOURNAL OF INFORMATION AND TELECOMMUNICATION 2024年第2期8卷 189-207页

作者： Saheed, Yakub Kayode Kehinde, Temitope Olubanjo Raji, Mustafa Ayobami Baba, Usman Ahmad Amer Univ Nigeria Sch IT & Comp Adamawa Nigeria Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Univ Texas Rio Grande Valley Dept Mkt Edinburg TX USA Pen Resource Univ Dept Comp Sci Gombe Nigeria

This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness.

关键词： Principal component analysis intrusion detection system bat algorithm Naive Bayes feature selection

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Static Reconfiguration of Unbalanced Distribution Systems with Variable Power using Selective bat algorithm

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2021年第3期32卷 656-671页

作者： Gerez, Cassio Costa, Eduardo C. Marques Sguarezi Filho, Alfeu J. Univ Sao Paulo EPUSP Polytech Sch Sao Paulo SP Brazil Fed Univ ABC UFABC Santo Andre SP Brazil

Distribution network reconfiguration (DNR) is one of the most studied and important optimization problems in Electrical Engineering field, being well established throughout the years, as seen in the specialized literature. The majority of studies treats the problem in a conservative way, i.e., considering the systems as balanced with fixed demand. However, the real characteristics of distribution systems may take into consideration the unbalanced nature of loads, as well as the variable demand characteristic according to the type of consumer connected. This comes closer to reflect the real operation of such systems, and thus, studies considering DNR with these aspects are becoming more important in recent years. In this context, the paper here presented proposes modeling unbalanced systems with variable demand using a static reconfiguration (SR). As SR considers the whole studied period to define one single set of switches, it is opposed to a dynamic reconfiguration (DR), which is performed in short periods. Considering the load profiles presented, a comparison is made to discuss the advantages and disadvantages of SR in the expanse of DR in unbalanced systems. To solve the DNR problem, a selective bat algorithm (Sbat) is used, as it presents characteristics from particle swarm optimization (PSO) and harmony search (HS). Tests for SR and DR with variable demand are performed in one balanced system (33 buses) and three unbalanced systems (19, 25 and 123 buses). Results for SR using Sbat are compared with selective PSO and selective HS.

关键词： Distribution network Static reconfiguration Dynamic reconfiguration bat algorithm Optimization

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

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PATTERN RECOGNITION 2023年 133卷

作者： Eskandari, S. Seifaddini, M. Univ Guilan Dept Comp Sci Rasht Iran

Streaming feature selection (SFS), is the task of selecting the most informative features in dealing with high-dimensional or incrementally growing problems. Several SFS algorithms have been proposed in the literature. However, they do not consider all feature subsets at the redundancy analysis step due to com-putational concerns. Moreover, they do not reconsider previously removed features which leads to losing most of the useful information. In this paper, the redundancy analysis step is defined as a binary opti-mization problem. Then, a binary bat algorithm (BBA) is adopted to find the minimal informative subsets. In this way, a large number of feature subsets can be considered effectively at the redundancy analysis step. In addition, an effective priority list is used to maintain previously removed redundant features. Such a list allows the re-examination of informative features. As a result, it is possible to consider the mutual information between features that are not streamed in an small time interval. Experimental stud-ies on fifteen different types of datasets show that our approach is superior to state-of-the-art online and offline streaming feature selection methods in terms of classification accuracy.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Feature selection Online feature selection Streamwise feature selection Dimension reduction bat algorithm

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Determining post-fire residual compressive strength of reinforced concrete shear walls using the bat algorithm

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STRUCTURES 2021年 32卷 651-661页

作者： Shadbahr, Elham Aminnejad, Babak Lork, Alireza Islamic Azad Univ Kish Int Branch Dept Civil Engn Kish Island Iran Islamic Azad Univ Roudehen Branch Dept Civil Engn Roudehen Iran Islamic Azad Univ Dept Civil Engn Safadasht Branch Tehran Iran

The effect of fire on the reinforced concrete structures' lateral load-bearing systems is not well established in literature and design codes. Since in case of an earthquake this influence can be crucial, in this study we aimed to predict post-fire residual compressive strength of concrete in shear walls using artificial neural network (ANN) models. The network parameters were fine-tuned using the bat optimization metaheuristic algorithm. The accuracy of the bat-based ANN models was validated by comparing their predictions with the particle swarm optimization (PSO) algorithm-based ANNs and multiple linear regression models. The results for bat-trained networks revealed a mean squared error (MSE) of 4.881 MPa, and prediction-target correlation of 0.987 on testing data which are more accurate than their PSO-trained counterparts.

关键词： Fire damage Reinforced concrete shear wall Artificial neural networks bat algorithm Particle swarm optimization

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Recognition of cavitation characteristics in non-clogging pumps based on the improved Lévy flight bat algorithm

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Lang, Tao Ni, Chen Chen, Keqiang Xu, Enxiang Yin, Jia Shen, Xi Wu, Xing Zhang, Desheng Jiangsu Univ Res Ctr Fluid Machinery Engn & Technol Zhenjiang Jiangsu Peoples R China

The performance and operational stability of non-clogging pumps can be affected by cavitation. To accurately identify the cavitation state of the non-clogging pump and provide technical references for monitoring its operation, a study was conducted on the optimization of Elman neural networks for cavitation monitoring and identification using the Improved Levy Flight bat algorithm (ILBA) on the basis of the traditional bat algorithm (BA). The ILBA employs multiple bats to interact and search for targets and utilizes the local search strategy of Levy flight, effectively avoiding local minima by taking advantage of the non-uniform random walk characteristics of large jumps. The ILBA algorithm demonstrates superior performance compared to other traditional algorithms through simulation testing and comparative calculations with eight benchmark test functions. On this basis, the optimization of the weights and thresholds of the Elman neural network was carried out by the improved bat algorithm. This leads to an enhancement in the accuracy of the neural network for identifying and classifying cavitation data, and the establishment of the ILBA-Elman cavitation diagnosis model was achieved. Collect pressure pulsation signals at the tongue of the non-clogging pump volute through cavitation tests. Through the cavitation feature extraction method based on Variational Mode Decomposition (VMD) and Multi-scale Dispersion Entropy (MDE), the interference signal can be effectively suppressed and the complexity of the time series can be measured from multiple angles, thereby creating a cavitation feature data set. The improved cavitation diagnosis model (ILBA-Elman) can realize the effective identification of the cavitation characteristics of non-clogging pumps through a variety of algorithm comparison experiments.

关键词： bat algorithm Levy flight Elman neural network cavitation recognition non-clogging pump VMD multi-scale dispersion entropy

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Optimal sizing of stand-alone hybrid photovoltaic/wind system using bat algorithm

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INTERNATIONAL JOURNAL OF AMBIENT ENERGY 2021年第8期42卷 920-928页

作者： Marsa, Nadia Houcine, Lassad Zaafouri, Abdurrahman Chaari, Abdelkader Univ Tunisia Lab Engn Ind Syst & Renewable Energies LISIER ENSIT Tunis Tunisia Univ Tunis Natl Higher Engn Sch Tunis ENSIT Tunis Tunisia

This paper presents a new optimal sizing methodology for a stand-alone PV-wind hybrid (PWH) energy system that is serving the electricity demand for an offshore petroleum platform. A bat optimisation technique is implemented to minimise the annualised cost of the system (ACS) considering power balance constraint and to determine the optimal number of PV panels, wind turbines and batteries. The comparison of bat algorithm with Homer and particle swarm optimisation algorithm is done in this paper and it shows that bat algorithm finds optimal sizing of PWH easily with lower cost. The results show that considering reliability characteristics has a major impact on optimal sizing, load supply level and cost.

关键词： bat algorithm PSO algorithm Homer photovoltaic energy wind energy

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A Quantum Annealing bat algorithm for Node Localization in Wireless Sensor Networks

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SENSORS 2023年第2期23卷 782-782页

作者： Yu, Shujie Zhu, Jianping Lv, Chunfeng Shanghai Ocean Univ Coll Engn Sci & Technol 999 Huchenghuan Rd Shanghai 201306 Peoples R China

Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%.

关键词： wireless sensor networks node localization bat algorithm geometric features quantum evolution tournament natural selection

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bat algorithm-based weighted Laplacian probabilistic neural network

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NEURAL COMPUTING & APPLICATIONS 2020年第4期32卷 1157-1171页

作者： Naik, Shraddha M. Jagannath, Ravi Prasad K. Kuppili, Venkatnareshbabu Natl Inst Technol Goa Dept Comp Sci & Engn Farmagudi Goa India Natl Inst Technol Goa Dept Appl Sci Farmagudi Goa India

Probabilistic neural network (PNN) is a single-pass feed-forward neural network with the capability of providing nonlinear decision boundaries. In this work, we propose the modifications to the existing PNN approach. Contributions are threefold: First, symmetric Laplace distribution has been used instead of Gaussian distribution in the pattern layer of the PNN approach. Second, a new weight coefficients' estimation method has been introduced between the pattern and summation layers of PNN. Third, a novel convex fitness function has been designed for the bat algorithm to obtain an optimal smoothing parameter vector. The designed fitness function maintains a balance between the sensitivity and specificity values. The performance of the proposed algorithm "bat algorithm-based weighted Laplacian probabilistic neural network" is compared with that of conventional PNN, weighted PNN, extreme learning method, optimal pruned extreme learning machine and K-nearest neighbor. Experimental evaluation is carried out on eleven benchmark data sets using different performance measures such as accuracy, sensitivity, specificity and Youden's index. The comparative performance evaluation of the proposed model has been made with the PNN, and other standard algorithms outperform the compared approaches in terms of classification accuracy. Friedman test is used for statistical evaluation of the proposed approach.

关键词： Probabilistic neural network Symmetric Laplace distribution bat algorithm Classification

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