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Global convergence analysis of the bat algorithm using a markovian framework and dynamical system theory

EXPERT SYSTEMS WITH APPLICATIONS 2018年 114卷 173-182页

作者： Chen, Si Peng, Guo-Hua He, Xing-Shi Yang, Xin-She Northwestern Polytech Univ Coll Nat & Appl Sci 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Xian Polytech Univ Coll Sci 19 Jinhua South Rd Xian 710048 Shaanxi Peoples R China Middlesex Univ Sch Sci & Technol London NW4 4BT England

The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm and to identify the possible effect of parameter settings on convergence, we have designed an updated model in terms of a dynamic matrix. Subsequently, we have used the stability theory of discrete-time dynamical systems to obtain the stable parameter ranges for the algorithm. Furthermore, we use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： bat algorithm Global convergence Markov chain theory Dynamical system theory Parameters selection Optimization Swarm intelligence

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Modified bat algorithm based on covariance adaptive evolution for global optimization problems

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SOFT COMPUTING 2018年第16期22卷 5215-5230页

作者： Shan, Xian Cheng, Huijin China Univ Petr Sch Sci Qingdao 266580 Peoples R China China Univ Petr Sch Econ & Management Qingdao 266580 Peoples R China

bat algorithm is a newly proposed swarm intelligence algorithm inspired by the echolocation behavior of bats, which has been successfully used in many optimization problems. However, due to its poor exploration ability, it still suffers from problems such as premature convergence and local optimum. In order to enhance the search ability of the algorithm, we propose an improved bat algorithm, which is based on the covariance adaptive evolution process. The information included in the covariance adaptive evolution diversifies the search directions and sampling distributions of the population, which is of great benefit to the search process. The proposed approaches have been tested on a set of benchmark functions. Experimental results indicate that the proposed algorithm obtains superior performance over the majority of the test problems.

关键词： bat algorithm Swarm intelligence Covariance adaptive evolution

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A modified bat algorithm with torus walk for solving global optimisation problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第1期15卷 1-13页

作者： Bangyal, Waqas Haider Ahmed, Jamil Rauf, Hafiz Tayyab Iqra Univ Dept Comp & Technol Islamabad Pakistan KUST Dept Comp & Technol Kohat Pakistan Univ Gujrat Dept Comp Sci Gujrat Pakistan

bat algorithm (BA) has been widely used to solve the diverse kinds of optimisation problems. In accordance with the optimisation problems, balance between the two major components: exploitation and exploration, plays a significant role in meta-heuristic algorithms. Several researchers have worked on the performance for the improvement of these algorithms. BA faces one of the major issues in high dimensions. In our work, we proposed a new variant of BA by introducing the torus walk (TW-BA) to solve this issue. To improve the local search capability instead of using the standard uniform walk, torus walk is incorporated in this paper. The simulation results performed on 19 standard benchmark functions depicts the efficiency and effectiveness of TW-BA compared with the traditional BA, directional bat algorithm, particle swarm optimisation, cuckoo search, harmony search algorithm, differential evolution and genetic algorithm. The promising experimental result suggests the superiority of proposed technique.

关键词： bat algorithm torus walk chaotic inertia weight exploitation exploration

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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 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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bat algorithm for Kernel Computation in Fractal Image Reconstruction 19th

Bat Algorithm for Kernel Computation in Fractal Image Recons...

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19th Annual International Conference on Computational Science (ICCS)

作者： Galvez, Akemi Osaba, Eneko Del Ser, Javier Iglesias, Andres Toho Univ Dept Informat Sci Fac Sci Narashino Campus2-2-1 Miyama Funabashi Chiba 2748510 Japan Univ Cantabria Dept Appl Math & Comp Sci ETSI Caminos Canales & Puertos Avda Castros S-N Santander 39005 Spain TECNALIA Res & Innovat Derio 48160 Spain Univ Basque Country UPV EHU Bilbao Spain

ISBN: (纸本)9783030227449;9783030227432

Computer reconstruction of digital images is an important problem in many areas such as image processing, computer vision, medical imaging, sensor systems, robotics, and many others. A very popular approach in that regard is the use of different kernels for various morphological image processing operations such as dilation, erosion, blurring, sharpening, and so on. In this paper, we extend this idea to the reconstruction of digital fractal images. Our proposal is based on a new affine kernel particularly tailored for fractal images. The kernel computes the difference between the source and the reconstructed fractal images, leading to a difficult nonlinear constrained continuous optimization problem, solved by using a powerful nature-inspired metaheuristics for global optimization called the bat algorithm. An illustrative example is used to analyze the performance of this approach. Our experiments show that the method performs quite well but there is also room for further improvement. We conclude that this approach is promising and that it could be a very useful technique for efficient fractal image reconstruction.

关键词： Image processing Image reconstruction Affine kernel Fractal image bat algorithm

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bat algorithm for Fingerprint Image Enhancement 12

Bat Algorithm for Fingerprint Image Enhancement

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12th IEEE International Conference on Programming and Systems (ISPS)

作者： Bouaziz, Amira Draa, Amer Chikhi, Salim Univ Constantine 2 MISC Lab Constantine Algeria

ISBN: (纸本)9781479977000

Fingerprint biometric systems performance is affected by the quality of fingerprint images. To overtake the low quality fingerprint images as well as overmaster the traditional image enhancers, the paper proposes a bat algorithm for gray scale fingerprint image contrast enhancement. The purpose is for the bat algorithm to map the gray level distributions for contrast enhancement ends. To assess the approach, the enhancement process is evaluated on low quality images from the FVC 2000 and compared to one of the traditional related-work contrast-based enhancers. The results show that the proposed bat algorithm has proven to qualitatively and numerically improve the fingerprint image quality through contrast manipulation on the general level of noise eradication and quality metrics in addition to ridge structure clarification and minutiae detection specificities.

关键词： component fingerprint recognition fingerprint image contrast enhancement bat algorithm gray level mapping

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DSTATCOM allocation in distribution networks considering load variations using bat algorithm

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AIN SHAMS ENGINEERING JOURNAL 2017年第3期8卷 391-403页

作者： Yuvaraj, T. Ravi, K. Devabalaji, K. R. VIT Univ Sch Elect Engn Vellore Tamil Nadu India

This paper proposes a new method of scheduling for optimal placement and sizing of Distribution STATic COMpensator in the radial distribution networks to minimize the power loss. In the proposed method Voltage Stability Index is used to search the optimal placement for installation of DSTATCOM. Optimal size of DSTATCOM is found by using bat algorithm. The feeder loads are varied by linearly from light load to peak load with a step size of 1%. In each load step, the optimal placement and sizing for DSTATCOM are calculated. By using the Curve Fitting Technique, the optimal sizing for DSTATCOM per load level is formulated in the form of generalized equation. The proposed approach will help the Distribution Network Operators to select the DSTATCOM size according to the load changes. To check the feasibility of the proposed method, system has been tested on two standard buses such as IEEE 33 and 69 bus radial distribution systems. (C) 2015 Ain Shams University. Production and hosting by Elsevier B.V.

关键词： Distribution STATic COMpensator (DSTATCOM) bat algorithm Voltage Stability Index (VSI) Distribution Network Operators (DNOs) Curve Fitting Technique (CFT)

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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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A Novel Jaya-bat algorithm Based Power Consumption Minimization in Cognitive Radio Network

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WIRELESS PERSONAL COMMUNICATIONS 2019年第4期108卷 2059-2075页

作者： Kaur, Avneet Sharma, Surbhi Mishra, Amit Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala Punjab India

The aim of this paper is to propose a new hybrid optimization technique, namely Jaya-bat algorithm (JBA) and to demonstrate its application for constrained power consumption minimization in cognitive radio network considering Class B power amplifier. JBA is motivated by recently developed Jaya algorithm (JA) having good exploration ability and nature inspired bat algorithm (BA) with good exploitation feature. In JBA, both JA and BA help each other to get away from local optimum solution and converge towards best optimal solution. The proposed algorithm when applied to different benchmark functions shows enhanced performance in comparison to other state-of-the-art metaheuristic techniques available in literature. Reconfiguration of transmission parameters for cognitive radio (CR) user supporting data transmission mode is carried out with a purpose of minimizing the power consumption while supporting different QoS requirements. The solutions show that the constrained optimization by cognitive decision module using JBA provides better results as compared to BA and JA based optimization techniques. It proves the potential of JBA as an efficient technique to be used for power consumption minimization problem in CR networks.

关键词： bat algorithm Class B power amplifier Cognitive radio Jaya algorithm Metaheuristic optimization

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