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 co...
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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.
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 abilit...
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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.
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 ...
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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.
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 w...
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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.
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 thro...
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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%.
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 re...
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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.
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 Ba...
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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.
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 Stabili...
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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.
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 ...
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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.
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 con...
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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.
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