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An Improved Spherical Vector and Truncated Mean Stabilization Based bat algorithm for UAV Path Planning

IEEE ACCESS 2023年 11卷 2396-2409页

作者： Chen, Buqian Yang, Jin Zhang, Huizhen Yang, Meng Univ Shanghai Sci & Technol Sch Sci Shanghai Peoples R China Univ Shanghai Sci & Technol Sch Management Shanghai Peoples R China

Unmanned aerial vehicles have a wide range of applications. An intelligent optimization algorithm based on the traditional bat algorithm (BA) is investigated in this paper for UAV flight path planning in a static complex environment. The primary goal of this work is to develop a safer flight path while considering the feasibility of the UAV and the requirements for safe operation. This research proposes an improved spherical coordinate and truncated average stable strategy-based bat optimization algorithm (TMS-SBA). The algorithm uses the UAV's motion space to encode the operator, and by substituting a new bat for the worst of the old one after each iteration to increase population diversity, the algorithm can converge quickly in a complex environment while maintaining stable operation. In addition, the flight path is smoothly generated by using B-spline curves to make the planned path suitable for UAV. MATLAB simulation experiments show that, compared with other traditional swarm intelligent algorithms, TMS-SBA can successfully generate feasible and effective optimal solutions in complex environments and plan shorter, safer, and more accessible flight paths for UAV.

关键词： Unmanned aerial vehicle (UAV) path planning bat algorithm configuration space truncated mean stabilization strategy

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A hybrid chaotic bat algorithm for optimal placement and sizing of dg units in radial distribution networks

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ENERGY REPORTS 2024年 12卷 1723-1741页

作者： Khenissi, Imene Guesmi, Tawfik Alshammari, Badr M. Alqunun, Khalid Almalaq, Abdulaziz Alturki, Mansoor Neji, Rafik Univ Sfax Natl Engn Sch Sfax LETI Lab Dept Elect Engn Sfax Tunisia Univ Hail Coll Engn Dept Elect Engn Hail 55211 Saudi Arabia

Appropriate location and sizing of distributed generation (DG) units in a radial distribution system (RDS) play a significant role in the improvement of its overall performance. Indeed, proper DG placement and sizing help in maintaining the balance between power supply and demand, decreasing energy losses and enhancing voltage profile. Within this context, this paper presents an effective approach for the determination of the optimal location and the appropriate capacity of a photovoltaic distributed generation (PVDG) unit in RDSs. In the suggested approach, the best position of the PVDG is selected using the loss sensitivity factor (LSF). Meanwhile, a new optimization technique incorporating chaos, bats' self-adaptive compensation, and Doppler effect into the original bat algorithm (BA) is developed to find the optimal size of the PVDG unit. The optimal PVDG size is optimally determined in such a way that total active power losses of the RDS is reduced and voltage profile is enhanced. The performance of the proposed optimization technique, symbolized by (CSA-DC)BA, is evaluated using various benchmark functions. Moreover, the applicability and effectiveness of the suggested approach are verified on the IEEE 33-bus and the IEEE 69-bus RDSs. Obtained results revealed that the proposed (CSA-DC)BA outperforms the comparison optimization techniques.

关键词： Distributed generation Radial distribution systems Loss sensitivity analysis Chaos theory bat algorithm Doppler effect

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Energy efficient fuzzy clustering and routing using bat algorithm

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WIRELESS NETWORKS 2021年第4期27卷 2813-2828页

作者： Lipare, Amruta Edla, Damodar Reddy Dharavath, Ramesh Natl Inst Technol Ponda 403401 Goa India Indian Inst Technol ISM Dhanbad Bihar India

Clustering of sensor nodes is one of the prominent methods applied to Wireless Sensor Networks (WSN). In the cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes are composed of limited battery power. Therefore, energy efficiency in WSN is crucial. A load of sensor node and its distance from base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSN. In this paper, we propose a load-balanced clustering algorithm using Fuzzy C means (FCM) algorithm and an energy-efficient routing approach using bat-algorithm (FC-Rbat). The cluster heads (CHs) are selected according to the score of the sensor node from each cluster. After selection of the CHs, the bat-inspired routing algorithm is applied on the CHs. The best routing path from each CH to the BS is obtained from the proposed approach. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network and the standard deviation of a load of the sensor node. It is observed that FC-Rbat outperforms compared algorithms, namely EAUCF, DUCF and SGA, under the evaluation factors.

关键词： bat algorithm Clustering Energy efficiency Fuzzy system Load balancing Wireless Sensor Networks

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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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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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Binary bat algorithm

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期25卷 663-681页

作者： Mirjalili, Seyedali Mirjalili, Seyed Mohammad Yang, Xin-She Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Shahid Beheshti Univ Fac Elect & Comp Engn Dept Elect Engn Tehran *** Iran Univ Cambridge Dept Engn Cambridge CB2 1PZ England

bat algorithm (BA) is one of the recently proposed heuristic algorithms imitating the echolocation behavior of bats to perform global optimization. The superior performance of this algorithm has been proven among the other most well-known algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). However, the original version of this algorithm is suitable for continuous problems, so it cannot be applied to binary problems directly. In this paper, a binary version of this algorithm is proposed. A comparative study with binary PSO and GA over twenty-two benchmark functions is conducted to draw a conclusion. Furthermore, Wilcoxon's rank-sum nonparametric statistical test was carried out at 5 % significance level to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The results prove that the proposed binary bat algorithm (BBA) is able to significantly outperform others on majority of the benchmark functions. In addition, there is a real application of the proposed method in optical engineering called optical buffer design at the end of the paper. The results of the real application also evidence the superior performance of BBA in practice.

关键词： bat algorithm Discrete evolutionary algorithms Binary optimization Optical buffer design Optimization Discrete optimization Bio-inspired algorithm

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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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Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System

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APPLIED SOFT COMPUTING 2015年 32卷 403-419页

作者： Premkumar, K. Manikandan, B. V. Pandian Saraswathi Yadav Engn Coll Dept Elect & Elect Engn Sivagangai 630561 Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi 626005 Tamil Nadu India

In this paper, speed control of Brushless DC motor using bat algorithm optimized online Adaptive Neuro Fuzzy Inference System is presented. Learning parameters of the online ANFIS controller, i.e., Learning Rate (eta), Forgetting Factor (lambda) and Steepest Descent Momentum Constant (alpha) are optimized for different operating conditions of Brushless DC motor using Genetic algorithm, Particle Swarm Optimization, and bat algorithm. In addition, tuning of the gains of the Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Fuzzy Logic Controller is optimized using Genetic algorithm, Particle Swarm Optimization and bat algorithm. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. Also, performance indices such as Root Mean Squared Error, Integral of Absolute Error, Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the Brushless DC motor. The real time experimental verification of the proposed controller is verified using an advanced DSP processor. The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： ANFIS controller bat algorithm Brushless DC motor Fuzzy PID controller PID controller

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Feature Selection in GPCR Classification Using bat algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2020年第1期19卷

作者： Bekhouche, Safia Ben Ali, Yamina Mohamed Badji Mokhtar Univ Dept Comp Sci Annaba 23000 Algeria Badji Mokhtar Univ LRI Annaba 23000 Algeria

G-Protein-Coupled Receptors (GPCR) are the large family of protein membrane;and until now some of them still remain orphans. Predicting GPCR functions is a challenging task, it depends closely to their classification, which requires a digital representation of each protein chain as an attribute vector. A major problem of GPCR databases is their great number of features which can produce combinatorial explosion and increase the complexity of classification algorithms. Feature selection techniques are used to deal with this problem by minimizing features space dimension, and keeping the most relevant ones. In this paper, we propose to use the bat algorithm for extracting the pertinent features and to improve the classification results. We compared the results obtained by our system with two other bio-inspired algorithms, Evolutionary algorithm and PSO search. Metrics quality measures used for comparison are Error Rate, Accuracy, MCC and F-measure. Experimental results indicate that our system is more efficient.

关键词： GPCR feature selection bat algorithm feature extraction strategies classification local descriptors data mining algorithms protein representation methods

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A damage identification technique for beam-like and truss structures based on FRF and bat algorithm

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COMPTES RENDUS MECANIQUE 2018年第12期346卷 1253-1266页

作者： Zenzen, Roumaissa Belaidi, Idir Khatir, Samir Wahab, Magd Abdel Univ Mhamed Bougara Boumerdes Dept Mech Engn LEMI Lab Res Team Modelling & Simulat Mech Engn Boumerdes 35000 Algeria Univ Ghent Fac Engn & Architecture Dept Elect Energy Syst & Automat Ghent Belgium Duy Tan Univ Inst Res & Dev Quang Trung 03 Da Nang Vietnam Univ Ghent Fac Engn & Architecture Soete Lab Technol Pk Zwijnaarde 903 B-9052 Zwijnaarde Belgium

In this paper, a Structural Health Monitoring (SHM) technique for damage identification in beam-like and truss structures using Frequency Response Function (FRF) data coupled with optimization techniques is presented. Genetic algorithm (GA) and bat algorithm (BA) are used to estimate the location and severity of damage. The damage in the structures is simulated by reduction in rigidity of specific members. Both optimization techniques are coupled with modelled structures using Finite Element Method (FEM). The approach is based on minimizing an objective function by comparing measured and calculated FRFs. The results show that better accuracy is obtained using BA than using GA in terms of precision and computational time. Furthermore, it is found that the proposed approach provides faster solution than other approaches in the literature. (C) 2018 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.

关键词： Damage identification Frequency Response Function Genetic algorithm Finite element method bat algorithm

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