In this paper the hybrid BA/TS is developed. This hybrid is merged from bee algorithm (BA) and tabu search (TS) to solve economic dispatch (ED) problem. The 6 units system and 10 units system are used to demonstrate f...
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ISBN:
(纸本)9781509052103
In this paper the hybrid BA/TS is developed. This hybrid is merged from bee algorithm (BA) and tabu search (TS) to solve economic dispatch (ED) problem. The 6 units system and 10 units system are used to demonstrate for the improvement of the proposed by comparing with general BA. The generator constraint is used to determine the power of generator of test system for normal operation. All test results indicate that the operating costs by using proposed method are lower than those from general BA and hence, hybrid BA/TS methods can result in economic dispatch problem. For economic dispatch problems, the hybrid BA/TS method is very effective. This means hybrid BA/TS is an alternative approaches than the general BA.
This paper describes the developed genetic, ant colony and bee algorithms for solving the following printed circuit board (PCB) design multi-criteria optimization problems: PCB elements packaging within modules, irreg...
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ISBN:
(纸本)9781538618103
This paper describes the developed genetic, ant colony and bee algorithms for solving the following printed circuit board (PCB) design multi-criteria optimization problems: PCB elements packaging within modules, irregularly shaped components placement on PCB, and multilayer PCB routing. The results of the simulation experiments confirm that the bionic algorithms have the better convergence and better design decisions quality in comparison with another well-known algorithms used for multi-criteria automated synthesis.
In this paper, we explore a heuristic method called the bee waggle dance to construct cryptographically strong S-boxes. The S-boxes exhibit good cryptographic properties such as high nonlinearity, low differential uni...
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In this paper, we explore a heuristic method called the bee waggle dance to construct cryptographically strong S-boxes. The S-boxes exhibit good cryptographic properties such as high nonlinearity, low differential uniformity and high algebraic degree. The method involves the use of a trinomial power function as the initial S-box. The elements in the S-box are then permuted using the bee waggle dance algorithm. The S-boxes produced using this method are found to compare reasonably well with other existing S-boxes constructed using alternative heuristic methods. To the best of our knowledge, this is the first attempt to construct cryptographically strong S-boxes using the bee waggle dance algorithm.
An integral terminal sliding mode controller is proposed in order to control chaos in a rod-type plasma torch *** this method, a new sliding surface is defined based on a combination of the conventional sliding surfac...
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An integral terminal sliding mode controller is proposed in order to control chaos in a rod-type plasma torch *** this method, a new sliding surface is defined based on a combination of the conventional sliding surface in terminal sliding mode control and a nonlinear function of the integral of the system states. It is assumed that the dynamics of a chaotic system are unknown and also the system is exposed to disturbance and unstructured uncertainty. To achieve a chattering-free and high-speed response for such an unknown system, an adaptive neuro-fuzzy inference system is utilized in the next step to approximate the unknown part of the nonlinear dynamics. Then, the proposed integral terminal sliding mode controller stabilizes the approximated system based on Lyapunov＇s stability theory. In addition, a bee algorithm is used to select the coefficients of integral terminal sliding mode controller to improve the performance of the proposed method. Simulation results demonstrate the improvement in the response speed, chattering rejection, transient response,and robustness against uncertainties.
In this work, we present a P2PBA algorithm that refers to a peer-to-peer file searching method, which uses the bees algorithm. The proposed algorithm has been designed with an aim towards providing an efficient peer-t...
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In this work, we present a P2PBA algorithm that refers to a peer-to-peer file searching method, which uses the bees algorithm. The proposed algorithm has been designed with an aim towards providing an efficient peer-to-peer (P2P) file search in mobile ad hoc networks (MANETs). With reference to Li et al. (2004) and Gerla and Lindemann (2005) it has been observed that the implementation of P2P file sharing system on MANETs is quite tricky to implement as compared to that on a wired network. With the advent of swarm intelligence, the P2P file sharing methodology not only found an optimized search process involving a more selective node tracing, but it also proved to improve the time efficiency and robustness of the sharing mechanism. The P2P file searching system implementation, particularly in a network of mobile nodes, poses: (a) the percentage network area scanned and (b) the selective file retrieval from a set of file bearing nodes as the biggest challenges. Managing nodes scattered over a large terrain is not easy. Node connectivity and file information become more volatile as the network area increases. Probability of retrieving a file from a profitable source is also a yardstick to determine how good the file retrieval algorithm is. This algorithm referred to as P2PBA implements the P2P file searching process using the bee algorithm (Pham et al., 2006b;Wedde and Farooq, 2005) and aims at solving these two challenges. This scheme of swarm-based intelligence, which is based on the foraging behavior of honey bees, optimizes the search process selectively hunting for more promising honey sources and scans a sizeable area in a more comprehensive manner. Following a description of the proposed algorithm, this paper finally presents the simulation results for the network against various specified parameters. The simulation results show that our algorithm proposes to make file searching much more efficient and improves the statistics against the posed challenges. (C) 2
This paper considers a closed-loop supply chain design problem including several producers, distributors, customers, collecting centers, recycle centers, revival centers, raw materials customers considering several pe...
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This paper considers a closed-loop supply chain design problem including several producers, distributors, customers, collecting centers, recycle centers, revival centers, raw materials customers considering several periods, existing inventory and shortage in distribution centers, and transportation cost and time. This problem is formulated as a bi-objective integer nonlinear programming model. The aim of this model is to determine numbers and locations of supply chain elements, their capacity levels, allocation structure, mode of transportation between them, amount of transported products between them, amount of existing inventory, and shortage in distribution centers in each period to minimize the sum of system costs and transportation time in the network. To validate this model and show the applicability of it for small-sized problems, GAMS software is used. Because this given problem is NP-hard, a bee Colony Optimization (BCO) algorithm is proposed to solve medium and large-sized problems. Furthermore, to examine the efficiency of the proposed BCO algorithm, the associated results are compared with the results obtained by the Genetic algorithm (GA). Finally, the conclusion is provided. (C) 2016 Sharif University of Technology. All rights reserved.
Multi-level inverter (MLI) is a promising technology, able to generate high-quality power with lower switching frequency. Therefore it leads to high conversion efficiency and low switching losses. Selective harmonic e...
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Multi-level inverter (MLI) is a promising technology, able to generate high-quality power with lower switching frequency. Therefore it leads to high conversion efficiency and low switching losses. Selective harmonic elimination is a fundamental frequency switching strategy that theoretically provides desirable output waveform for MLIs by elimination of low order harmonics. Unfortunately, complexity, non-linearity and solvability features attached to this method have limited its industrial application. In this study, a particle swarm optimisation (PSO)-based memetic algorithm (MA) guided by mesh adaptive direct search is introduced as a suitable choice for the harmonic optimisation problem. This algorithm is precise and applicable to any MLI. The results show that MA converges to the exact solution for feasible modulation index. When the problem has no exact solution, the algorithm finds a relatively proper solution to regulate the fundamental component of the voltage. Furthermore, the comparison between MA and other methods shows that the probability of convergence of MA is higher than others. For 48% of the analysed data MA reaches to a fitness value lower than 10(-30) whereas this probability is 5% for PSO and almost zero for genetic algorithm and bee algorithm. The proposed method has been implemented on a cascade seven-level inverter.
The determination of the rock types from which the water is recharged/discharged is an essential component of hydrochemical, hydrogeological and water pollution studies. Especially, detection of sources of groundwater...
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The determination of the rock types from which the water is recharged/discharged is an essential component of hydrochemical, hydrogeological and water pollution studies. Especially, detection of sources of groundwater contamination is very important in terms of human health and other living organism. This study aims at prediction of water pollution sources using artificial neural networks (ANNs) in Sivas, Karabuk and Bartin areas of Turkey, which have different types of rocks, agricultural activity and mining activity. In this study, a model based on ANNs was developed for forecast to the water discharging from different types of rocks and the water pollution sources in the study areas. Back propagation and bee algorithm (BA) were used in ANN training. For achieving the aim of the study, 14 hydrochemical data set were used. The best ANN classification of water discharging from different type of rocks was accomplished with 80 % accuracy using BA. These results indicate that the researches that are similar to this study can provide quite convenience for the assessment of groundwater pollution sources when applied on a large and regional scale.
Data clustering is one of widely used methods for data mining. The k-means approach is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. But some hindrances such as the...
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ISBN:
(纸本)9781479981144
Data clustering is one of widely used methods for data mining. The k-means approach is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. But some hindrances such as the sensitivity to initial values and cluster centers or the risk of trapping in local optimal reduce its best performance. The purpose of kmeans method is minimizing the dissimilarity of observations, from cluster centers. In this paper, a new solution method inspired by harmony search combined with bee algorithm is introduced to improve performance k-means clustering. In this study, harmony and clustering structures are combined to produce harmony clustering. To avoid initial random selection, seed cluster center is considered in primary population as well as bee algorithm has been employed to increase the efficiency of algorithm. The proposed methods have been tested on standard benchmark data sets and also compared to other methods in the literature;it is noted that results show a promising performance leading to better efficiency and capability of the proposed solution.
Dynamic or adaptive thresholding strategy is of high interest in pattern recognition, signal and image processing. In this article a powerful method using a combination of multilevel thresholding algorithm, bee algori...
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ISBN:
(纸本)9781479911806
Dynamic or adaptive thresholding strategy is of high interest in pattern recognition, signal and image processing. In this article a powerful method using a combination of multilevel thresholding algorithm, bee algorithm (BA), and hierarchical evolutionary algorithm (HEA) is proposed for segmentation of magnetic resonance images (MRIs). The HEA can be viewed as a modified variant of basic genetic algorithm (GA). The proposed method is based on the BA and, in fact, is an unsupervised clustering method depending on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not require to be known previously. The results show that the accuracy of the proposed algorithm is very excellent (about 97%).
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