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SAI Intelligent Systems Conference (IntelliSys)

作者： Imane, Messaoudi Nadjet, Kamel USTHB Algiers Algeria Univ Ferhat Abbes Setif 1 Fac Sci Dept Comp Sci Setif Algeria

ISBN: (纸本)9781467376068

In social network, a group of elements sharing common interests is called community. To know the structure of these communities, many works have been proposed with different techniques;we can cite node based methods and link based methods. This structure is complex where communities overlap every time. In this paper we use bat algorithm to discover overlapping communities. bat algorithm is a novel metaheuristic characterized by the echolocation behavior of bats. The algorithm we propose in this paper is based on the links of the network. The objective function evaluates the link density which is convenient for overlapping communities. Experiment on real networks show that the communities discovering with our approach have a higher density.

关键词： overlapping community link community bat algorithm

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bat algorithm for Attractor Reconstruction of Low-Dimensional Chaotic Maps from Time Series 48

Bat Algorithm for Attractor Reconstruction of Low-Dimensiona...

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Galvez, Akemi Perez-Carabaza, Sara Iglesias, Andres Univ Cantabria Dept Appl Math & Comp Sci Santander Spain

ISBN: (纸本)9798350376975;9798350376968

Reconstructing the attractors of unknown chaotic systems from time series data presents a formidable challenge with broad applications across various disciplines. In this paper, we propose a swarm intelligence approach to address this challenge, focusing specifically on low-dimensional chaotic maps. Our approach is based on the bat algorithm, a renowned bio-inspired optimization technique well-suited for continuous optimization tasks. We evaluate the effectiveness and validity of our proposed approach by applying it to two distinct examples of chaotic maps: the Burger map and the Duffing map. Through comprehensive experimentation, we showcase the satisfactory performance of our method in reconstructing attractors from time series data. Based on our empirical findings, we conclude that our approach holds significant promise for the reconstruction of attractors of low-dimensional chaotic maps using time series data.

关键词： Dynamical systems chaotic maps attractor reconstruction time series swarm intelligence bat algorithm

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bat algorithm, Genetic algorithm and Shuffled Frog Leaping algorithm for Designing Machine Layout

Bat Algorithm, Genetic Algorithm and Shuffled Frog Leaping A...

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7th Multi-Disciplinary International Workshop on Artificial Intelligence (MIWAI)

作者： Dapa, Kittipong Loreungthup, Pornpat Vitayasak, Srisatja Pongcharoen, Pupong Naresuan Univ Dept Ind Engn Ctr Operat Res & Ind Applicat Fac Engn Phitsanulok 65000 Thailand

ISBN: (纸本)9783642449499;9783642449482

Arranging non-identical machines into a limited area of manufacturing shop floor is an essential part of plant design. Material handling distance is one of the key performance indexes of internal logistic activities within manufacturing companies. It leads to the efficient productivity and related costs. Machine layout design is known as facility layout problem and classified into non-deterministic polynomial-time hard problem. The objective of this paper was to compare the performance of bat algorithm (BA), Genetic algorithm (GA) and Shuffled Frog Leaping algorithm (SFLA) for designing machine layouts in a multiple-row environment with the aim to minimise the total material handling distance. An automated machine layout design tool has been coded in modular style using a general purpose programming language called Tcl/Tk. The computational experiment was designed and conducted using four MLD benchmark datasets adopted from literature. It was found that the proposed algorithms performed well in different aspects.

关键词： Machine layout bat algorithm Genetic algorithm Shuffled Frog Leaping Multiple-row Parameter setting

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bat algorithm for optimizing a working vacation queue with impatient clients and secondary service

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OPSEARCH 2024年 1-20页

作者： Jyothsna, K. Kumar, P. Vijaya Laxmi, P. Vijaya GSS GITAM Dept Math Visakhapatnam 530045 Andhra Pradesh India Andhra Univ Dept Appl Math Visakhapatnam Andhra Pradesh India

This article investigates a Markovian working vacations queue with impatient clients and optional service. An arriving client can choose either to enter or not to enter the queue with a certain probability. Due to impatience, he may renege after joining the queue as per Poisson distribution. Two different kinds of services are offered by the server, namely, mandatory service which is required by all the clients entering the system and optional service which is opted by only few clients with certain probability after mandatory service. Just at the moment the system gets depleted, the server temporarily exits from the system for a short span called working vacations. Rather than shutting down completely throughout the working vacation duration, service is delivered at a different rate. The times between successive arrivals, vacation durations, service durations during mandatory service, during optional service and during working vacations are exponentially distributed and independent. Probability generating functions are used to evaluate the model's steady-state probabilities. The optimum service rate during mandatory service is obtained using bat algorithm. A table and a few graphs provide the numerical interpretations of the model for the impact of various model parameters on the system characteristics.

关键词： Mandatory service Optional service Working vacations Balking Reneging bat algorithm

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bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2016年第2期19卷 818-840页

作者： Premkumar, K. Manikandan, B. V. Rajalakshmi Inst Technol Dept Elect & Elect Engn Madras Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi 626005 Tamil Nadu India

In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation. (C) 2016, Karabuk University. Publishing services by Elsevier B.V.

关键词： bat algorithm Brushless direct current motor Cuckoo search Fuzzy proportional derivative controller Fuzzy proportional derivative integral controller

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bat algorithm for Topology Optimization in Microelectronic Applications

Bat Algorithm for Topology Optimization in Microelectronic A...

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1st International Conference on Future Generation Communication Technologies (FGCT)

作者： Yang, Xin-She Karamanoglu, Mehmet Fong, Simon Middlesex Univ Sch Sci & Technol Hendon Campus London NW4 4BT England Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Taipa Macao Peoples R China

ISBN: (纸本)9781467358613;9781467358590

In many design applications, designers often have to find the best geometrical configurations so as to achieve certain objectives with the minimum amount of materials used. Such shape or topology optimization problems are usually much harder to solve than nonlinear optimization problems in a fixed domain. In this paper, we use the recently developed bat algorithm to solve topology optimization problems. Results show that the distribution of different topological characteristics such as materials can be achieved efficiently. We have also tested the bat algorithm by solving nonlinear design benchmarks. Results suggest that bat algorithm is very efficient for solving nonlinear global optimization problems as well as topology optimization.

关键词： bat algorithm inverse problem optimization algorithm topology optimization metaheuristics

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bat algorithm for Designing Cell Formation with a Consideration of Routing Flexibility

Bat Algorithm for Designing Cell Formation with a Considerat...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Parika, Wipada Seesuaysom, Wipada Vitayasak, Srisatja Pongcharoen, Pupong Naresuan Univ Fac Engn Dept Ind Engn CORIA Phitsanulok 65000 Thailand

ISBN: (纸本)9781479909865

Cellular manufacturing system (CMS) is an approach that can be used to enhance both flexibility and efficiency in small-to-medium lot production environment. In cell formation, machines are grouped into cells based on their contributions to manufacturing process. Flexible routes have resulted in different machine sequences causing the changes in the movement of parts between cells. The movements in manufacturing shop floor lead to the efficiency of productivity relating to costs. Cell formation problem is classified into non-deterministic polynomial-time hard problem, of which the amount of computation required to find solutions increases exponentially with problem size. Solving this kind of problem by full numerical methods especially for the large size problem can be computationally expensive. The objectives of this paper were to describe the application of bat algorithm (BA) for designing cell formation aiming to minimise inter-cell part movement with a consideration of routing flexibility and investigate the appropriate setting of BA parameters that have an effect to the solution quality. A cell formation designing program was coded in modular style using a general purpose programming language called Tcl/Tk. The computational experiment was designed and conducted using ten datasets, in which the number of machines in each cell is either equal or unequal. The statistical analysis on the experimental results suggested that the population size and the number of iterations have statistical impact on the quality of the solutions obtained.

关键词： CMS Cell formation bat algorithm Routing flexibility Inter-cell movement

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bat algorithm Method for Automatic Determination of Color and Contrast of Modified Digital Images 44

Bat Algorithm Method for Automatic Determination of Color an...

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44th Annual IEEE-Computer-Society International Conference on Computers, Software, and Applications (COMPSAC)

作者： Galvez, Akemi Iglesias, Andres Osaba, Eneko Del Ser, Javier Toho Univ Funabashi Chiba Japan Univ Cantabria Santander Spain Basque Res & Technol Alliance BRTA TECNALIA Derio Spain Univ Basque Country Bilbao Spain

ISBN: (纸本)9781728173030

This paper presents a new artificial intelligence-based method to address the following problem: given an initial digital image (source image), and a modification of the image (mod image) obtained from the source through a color map and visual attributes assumed to be unknown, determine suitable values for color map and contrast such that, when applied to the mod image, a similar image to the source is obtained. This problem has several applications in the fields of image restoration and cleaning. Our approach is based on the application of a powerful swarm intelligence method called bat algorithm. The method is tested on an illustrative example of the digital image of a famous oil painting. The experimental results show that the method performs very well, with a similarity error rate between the source and the reconstructed images of only 8.37%.

关键词： swarm intelligence bat algorithm image proessing digital image visual attributes

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Association Rule Mining Based on bat algorithm

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第7期12卷 1195-1200页

作者： Heraguemi, Kamel Eddine Kamel, Nadjet Drias, Habiba Univ Setif Dept Comp Sci Fac Sci Setif 41025 Algeria USTHB Dept Comp Sci Algiers 16111 Algeria

Data mining is the process of extracting useful knowledge from a large database by using software and tools to look for discrimination and expressive patterns. This process helps companies to focus on important information in their historical databases to make decisions. Association rule mining is one of the most important domain in data mining. It aims to extract correlations, frequent pattern and associations between the items in databases In this paper, we propose a bat-based algorithm (BA) for association rule mining (ARM bat). Our algorithm aims to maximize the fitness function to generate the best rules in the defined dataset starting from specific minimum support and minimum confidence. The efficiency of our proposed algorithm is tested on several generic datasets with different number of transactions and items. The results are compared to FPgrowth algorithm results on the same datasets. ARM bat algorithm perform better than the FPgrowth algorithm in term of computation speed and memory usage.

关键词： Association Rules Mining ARM Bio-Inspired Approaches bat algorithm Optimization FP-Growth algorithm Support Confidence

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Multi-level image thresholding using Otsu and chaotic bat algorithm

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NEURAL COMPUTING & APPLICATIONS 2018年第12期29卷 1285-1307页

作者： Satapathy, Suresh Chandra Raja, N. Sri Madhava Rajinikanth, V. Ashour, Amira S. Dey, Nilanjan Anil Neerukonda Inst Technol & Sci Dept Comp Sci & Engn Visakhapatnam 531162 Andhra Pradesh India St Josephs Coll Engn Dept Elect & Instrumentat Engn Chennai 600119 Tamil Nadu India Tanta Univ Dept Elect & Elect Commun Engn Fac Engn Tanta Egypt Techno India Coll Technol Dept Informat Technol Kolkata India

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512 x 512) and (481 x 321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.

关键词： Multi-level thresholding bat algorithm Otsu method Ikeda Map Peak signal to noise ratio (PSNR) Structural similarity index (SSIM)

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