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The improved elite multi-parent hybrid optimization algorithm based on bat algorithm to optimum design of automobile gearbox

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JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2018年第2期21卷 513-517页

作者： Wang, Chao Hunan Univ Arts & Sci Coll Mech Engn Changde 415000 Peoples R China

bat algorithm is a new intelligent optimization algorithm that is simple and easy to implement. But bat algorithm is easy to fall into local optimum and will appear premature convergence to lead to poor convergence precision. The elite multi-parent hybrid optimization algorithm is better than other optimization algorithms when solving complex function optimization problems. However, the algorithm consists of hybrid operation without mutation so as not to keep the diversity of population in the search process. Combining bat algorithm with elite multi-parent evolutionary optimization algorithm, the improved elite multi-parent hybrid optimization algorithm optimizing hybrid discrete variables was proposed. In this algorithm, firstly the rough optimization is carried out by bat algorithm, and then the accurate optimization is implemented by the elite multi-parent hybrid optimization algorithm. 'This kind of algorithm takes advantage of two algorithms and overcomes their shortcomings. The procedure as DIEMPCOA1.0 is to optimum design for three-shaft four-speed automobile gearbox with 20 design variables, 50 inequality constraints and eight equations. Optimization example shows that this algorithm has characteristics of no special requirements for the optimization design problems, better universality, reliable operation, higher calculation efficiency and stronger global convergence ability, so as to shorten the design cycle, reduce quality, reduce cost and improve quality.

关键词： bat algorithm elite multi-parent evolutionary optimization algorithm gearbox optimization elite-preservation hybrid discrete variable

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A bat algorithm with Generalized Walk for the Two-Stage Hybrid Flow Shop Problem

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INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2015年第3期7卷 1-16页

作者： Dekhici, Latifa Belkadi, Khaled Univ Sci & Technol Oran Math & Comp Sci Fac Oran Algeria

In the last years, a set of bio-inspired metaheuristics has proved their efficiencies in combinational and continues optimization areas. This paper intends to hybrid a discrete version of bat algorithm (BA) with Generalized Evolutionary Walk algorithm (GEWA) to solve the mono-processors two stages Hybrid Flow Shop scheduling. The authors compare the modified bat algorithm with the original one, with Particle Swarm Optimization (PSO) and with others results taken from literature. Computational results on a standard two-stage hybrid flow shop benchmark of 70 cases, and about 1700 instances, indicate that the proposed algorithm finds the best makespan (Cmax) in a good processing time comparing to the original bat algorithm and other algorithms.

关键词： bat algorithm Generalized Evolutionary Walk algorithm Makespan Particle Swarm Optimization Two Stages Hybrid Flow Shop

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Dynamic frequency based parallel k-bat algorithm for massive data clustering (DFBPKBA)

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第4期9卷 866-874页

作者： Tripathi, Ashish Kumar Sharma, Kapil Bala, Manju Delhi Technol Univ Delhi India IP Coll Women Delhi India

In the past one decade there has been significant increase in the growth of digital data. Therefore, good data mining techniques are important for the better decision making. Clustering is one of the key element in the field of data mining. K-means is a very popular algorithm present in the literature which is widely used for the clustering purpose. However k-means algorithm suffers from the problem of stucking into local optimum solution because of it's dependency on the random initialization of initial cluster center. In this paper a novel variant of bat algorithm based on dynamic frequency is introduced. Further the proposed variant is hybridized with K-means to present a new approach for clustering in distributed environment. Since evolutionary computation is very computation intensive, traditional sequential algorithms are not able to provide satisfactory results within the reasonable amount of time for the large scale data problems. To mitigate this problem the proposed variant is parallelized using the MapReduce model in the Hadoop framework. The experimental results show that the proposed algorithm has outperformed K-means, PSO and bat algorithm on eighty percent of the benchmark datasets in terms of intra-cluster distance. Further DBPKBA has also achieved significant speedup for dealing with massive datasets with increase in the number of nodes.

关键词： bat algorithm Hadoop MapReduce Large data sets DFBPKBA

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A novel hybrid bat algorithm with a fast clustering-based hybridization

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EVOLUTIONARY INTELLIGENCE 2020年第3期13卷 427-442页

作者： Eskandari, Sadegh Javidi, Mohammad Masoud Univ Guilan Fac Math Sci Rasht Iran Shahid Bahonar Univ Kerman Kerman Iran

bat algorithm (BA) is a new and promising metaheuristic search algorithm which could outperform existing algorithms. However, BA can be easily trapped in a local optimum regarded to low exploration ability. The present study proposed a new local-search-based hybrid heuristic to escape such scenario. The proposed hybrid BA (hBA) uses a clustering-based hybridization method which detects the early convergence of BA population by analyzing similarities among individuals. The main motivation for such an analysis is that when BA is continually converging, the similarity among individuals becomes higher. The proposed hBA is extensively evaluated on CEC2017 benchmark suite. The Experiments demonstrate that the algorithm achieves better results than continues variants of BA in every way. Moreover, as a case study, a binary version of the proposed hBA (hBBA) is applied to the well-known feature selection problem. The recorded results on 13 datasets demonstrate that hBBA would be considered as a new state-of-art in metaheuristic-based wrapper feature selection methods.

关键词： bat algorithm Metaheuristic search Nature inspired search Feature selection

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Optimal Placement and Sizing of DER's with Load Variations Using bat algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第6期39卷 4891-4899页

作者： Yammani, Chandrasekhar Maheswarapu, Sydulu Kumari, Matam Sailaja Natl Inst Technol Dept Elect Engn Warangal Andhra Pradesh India

This paper presents an algorithm for optimal placement and size of the distributed energy resources (DERs) considering loss minimization, voltage profile improvement, and line flow capacity as multi-objectives. DERs are the energy resources which contain renewable energy resources such as wind, solar and fuel cell, and some artificial models such as microturbines, gas turbines, diesel engines, sterling engines, and internal combustion reciprocating engines. Combinations of DER studies and for every combination, indices, active and reactive losses, and voltage profiles are studied. To optimize the objective function, new optimization technique called bat algorithm (BA) is proposed. The work is tested on 38-bus distribution system with different % of loading such as 90, 100, and 110 % of base-load condition. With BA, the voltage profile of the system and loss reduction with different loading conditions are presented. For all cases, current injection-based distribution load flow method is used.

关键词： Distributed energy resource (DER) Distribution systemloading Loss minimization Optimization algorithm bat algorithm

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Hybridizing Artificial Bee Colony with bat algorithm for Web Service Composition

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Computer Systems Science & Engineering 2023年第8期46卷 2429-2445页

作者： Tariq Ahamed Ahanger Fadl Dahan Usman Tariq Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Management Information Systems College of Business Administration-Hawtat Bani TamimPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology Al-TurbahTaiz UniversityTaiz9674Yemen

In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these *** WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)*** challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS *** this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and bat algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global *** bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence *** performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different *** datasets are created from real-world datasets and artificially to form different scale sizes of WSC *** results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to *** signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.

关键词： Internet of things artificial bee colony bat algorithm elitist strategy web service composition

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Optimization of Cogging Torque Based on the Improved bat algorithm

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023年第2期16卷 1-19页

作者： Bai, Wenbo Ran, Huajun China Three Gorges Univ Coll Elect & New Energy Yichang Peoples R China

Permanent magnet motors have the advantages of high output torque, high efficiency, and low noise, but the cogging effect is obvious. The 24-slot 4-pole surface-mounted permanent magnet synchronous motor is taken as an example to reduce the cogging torque of permanent magnet synchronous motors. Firstly, the generation mechanism of cogging torque is analysed based on the energy method, and the pole arc coefficient, air gap length, magnetic pole eccentricity, permanent magnet thickness, and slot opening width are determined as optimisation parameters. Then, a cogging torque optimisation method is further proposed based on the Taguchi method and the response surface method, and the bat algorithm with the Levy flight feature is applied to obtain the optimal solution for the response surface model. Finally, finite element software is used to simulate the optimal motor model. The experimental results show that the efficiency of the motor solved by optimal parameters is increased by 1.6%, the cogging torque is reduced by 82.16%, and the torque ripple is reduced by 8.2%. The optimisation of cogging torque in this paper avoids fluctuations in torque, reduces motor vibration and noise, and improves the control characteristics of the permanent magnet motors drive system, operational reliability, and low-speed performance in the motor speed control system and high accuracy positioning in the position control system.

关键词： Cogging Torque Taguchi Method Response Surface bat algorithm Levy Flight

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A Manhattan distance-based binary bat algorithm vs. integer ant colony optimisation for intrusion detection in the audit trails

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第4期18卷 424-437页

作者： Guendouzi, Wassila Boukra, Abdelmadjid USTHB LSI Lab Fac Elect & Comp Sci Algiers Algeria

Intrusion detection system (IDS) is the process of monitoring and analysing security activities occurring in computer or network systems. The detection method can perform either anomaly-based or misuse-based detection. The misuse mechanism aims to detect predefined attack scenarios in the audit trails, whereas the anomaly detection mechanism aims to detect deviations from normal user behaviour. In this paper, we deal with misuse detection. We propose two approaches to solve the NP-hard security audit trail analysis problem. Both rely on the Manhattan distance measure to improve the intrusion detection quality. The first proposed method, named enhanced binary bat algorithm (EBBA), is an improvement of bat algorithm (BA). The second one, named enhanced integer ant colony system (EIACS), is a combination of two metaheuristics: ant colony system (ACS) and simulated annealing (SA). Experiment results indicate that, for large problem size, the performance of EIACS is more significant than EBBA.

关键词： intrusion detection security audit trail analysis combinatorial optimisation problem NP-hard Manhattan distance bat algorithm ant colony system ACS simulated annealing

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New binary bat algorithm for solving 0-1 knapsack problem

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COMPLEX & INTELLIGENT SYSTEMS 2018年第1期4卷 31-53页

作者： Rizk-Allah, Rizk M. Hassanien, Aboul Ella Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Cairo Univ Fac Comp & Informat Giza Egypt Sci Res Grp Egypt Cairo Egypt

This paper presents a novel binary bat algorithm (NBBA) to solve 0-1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm enables the bats to enhance the exploration capability while LSS aims to boost the exploitation tendencies and, therefore, it can prevent the BBA-LSS from the entrapment in the local optima. Moreover, the LSS starts its search from BBA found so far. By this methodology, the BBA-LSS enhances the diversity of bats and improves the convergence performance. The proposed algorithm is tested on different size instances from the literature. Computational experiments show that the BBA-LSS can be promise alternative for solving large-scale 0-1 knapsack problems.

关键词： bat algorithm Local search scheme Knapsack problem

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ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and bat algorithm

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SENSING AND IMAGING 2016年第1期17卷 179-194页

作者： Kora, Padmavathi Krishna, K. Sri Rama Gokaraju Rangaraju Inst Engn & Technol Dept ECE Hyderabad Telangana India VR Siddhartha Engn Coll Dept ECE Vijayawada Andhra Pradesh India

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

关键词： Atrial fibrillation Wavelet coherence bat algorithm

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