This paper presents a hybrid heuristic based on the bees algorithm combined with the fix-and-optimize heuristic to solve the multi-level capacitated lot-sizing problem. The bees algorithm can be used as a new method t...
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This paper presents a hybrid heuristic based on the bees algorithm combined with the fix-and-optimize heuristic to solve the multi-level capacitated lot-sizing problem. The bees algorithm can be used as a new method to determine the sequence in which to apply the partition in the fix-and-optimize approach. This new manner of choosing the partition adds diversity to the solution pool and yields different local optima solutions after some iterations. The bees-and-fix-and-optimize (BFO) algorithm attempts to avoid these local optima by performing random search in accordance with the concept of bees algorithm. The BFO has yielded good results for instances from the literature and, in most cases, the results are superior to the best results provided by approaches presented in recent literature. They show that this construction concept is advantageous and illustrate the efficiency of hybrid methods composed of matheuristics and metaheuristics. Furthermore, the BFO approach is a general-purpose heuristic that can be applied to solve other types of production planning problems.
This paper presents the result of research in developing a novel training model for Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial Neural Networks with the Takagi-S...
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This paper presents the result of research in developing a novel training model for Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial Neural Networks with the Takagi-Sugeno Fuzzy Inference System to approximate nonlinear functions. Therefore, it is considered as a Universal Estimator. The original algorithm used in ANFIS training process has a hybrid model that uses Steepest Decent Derivative;therefore, it inherits low convergence rate and local minima during training. In this study, a training algorithm is proposed that combines bees algorithm (BA) and Least Square Estimation (LSE) (BA-LSE). The local and global exploration of BA as integrates with the best-fit solution of the LSE improves current shortcomings of ANFIS training process. The proposed training algorithm is examined under three different scenarios of function approximation, time series prediction, and classification experiments in order to verify the promising improvements in the training process of ANFIS. The experimental results validate high generalization capabilities of the BA-LSE training algorithm in comparison to the original hybrid training model of ANFIS. The new training model also enhances local minima avoidance and has high convergence rate.
This study investigates the application of operational modal analysis along with bees optimization algorithm for updating the finite element model of structures. bees algorithm applies instinctive behavior of honeybee...
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This study investigates the application of operational modal analysis along with bees optimization algorithm for updating the finite element model of structures. bees algorithm applies instinctive behavior of honeybees as they look for nectar of flowers. The parameters that needed to be updated are uncertain parameters such as geometry and material properties of the structure. To determine these uncertain parameters, local and global sensitivity analyses have been performed. An objective function is defined based on the sum of the squared errors between the natural frequencies obtained from operational modal analysis and finite element method. The natural frequencies of physical structure are determined by stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. To verify the accuracy of this method, the proposed algorithm is implemented on a three-story structure to update parameters of its finite element model. Moreover, to study the efficiency of bees algorithm, its results are compared with those of the particle swarm optimization, and Nelder and Mead methods. The comparison indicates that this algorithm leads more accurate results with faster convergence.
Brain-computer interface (BCI) systems need to work in real-time with large amounts of data, which makes the channel selection procedures essential to reduce over-fitting and to increase users' comfort. In that se...
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ISBN:
(纸本)9783319591476;9783319591469
Brain-computer interface (BCI) systems need to work in real-time with large amounts of data, which makes the channel selection procedures essential to reduce over-fitting and to increase users' comfort. In that sense, metaheuristics based on swarm intelligence (SI) have demonstrated excellent performances solving complex optimization problems and, to the best of our knowledge, they have not been fully exploited in P300-BCI systems. In this study, we propose a modified SI method, called binary bees algorithm (b-BA), that allows users to select the most relevant channels in an evolutionary way. This method has been compared to particle swarm optimization (PSO) and tested with the 'III BCI Competition 2005' dataset II. Results show that b-BA is suitable for use in this kind of systems, reaching higher accuracies (mean of 96.0 +/- 0.0%) than PSO (mean of 93.5 +/- 2.1%) and the original ones (mean of 94.0 +/- 2.8%) using less than the half of the initial channels.
Although bees algorithm (BA) has presented great performance so far in optimizing different problems, it still has weak convergence rate in some optimizations. Therefore, in this investigation in order to resolve its ...
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ISBN:
(纸本)9781538695692
Although bees algorithm (BA) has presented great performance so far in optimizing different problems, it still has weak convergence rate in some optimizations. Therefore, in this investigation in order to resolve its weakness and boost the exploitation of algorithm, it was hybridized by Firefly algorithm (FA). The hybrid bees and firefly algorithm (BA-FA) was implemented on twelve numerical benchmark functions to evaluate it. The BA-FA was also compared with four famous algorithms, including particle swarm optimization (PSO), invasive weed optimization (IWO) and so on. The results showed that BA-FA had the best performance and convergence in optimizing the mentioned functions.
The aim of multimodal optimisation is to find significant optima of a multimodal objective function including its global optimum. Many real-world applications are multimodal optimisation problems requiring multiple op...
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The aim of multimodal optimisation is to find significant optima of a multimodal objective function including its global optimum. Many real-world applications are multimodal optimisation problems requiring multiple optimal solutions. The bees algorithm is a global optimisation procedure inspired by the foraging behaviour of honeybees. In this paper, several procedures are introduced to enhance the algorithm's capability to find multiple optima in multimodal optimisation problems. In the proposed bees algorithm for multimodal optimisation, dynamic colony size is permitted to automatically adapt the search effort to different objective functions. A local search approach called balanced search technique is also proposed to speed up the algorithm. In addition, two procedures of radius estimation and optima elitism are added, to respectively enhance the bees algorithm's ability to locate unevenly distributed optima, and eliminate insignificant local optima. The performance of the modified bees algorithm is evaluated on well-known benchmark problems, and the results are compared with those obtained by several other state-of-the-art algorithms. The results indicate that the proposed algorithm inherits excellent properties from the standard bees algorithm, obtaining notable efficiency for solving multimodal optimisation problems due to the introduced modifications.
The main aim of this work is to introduce a novel approach to design and optimize of composite drive shafts based on bees algorithm (BA). BA was performed on a specific filament wound composite drive shaft which was s...
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The main aim of this work is to introduce a novel approach to design and optimize of composite drive shafts based on bees algorithm (BA). BA was performed on a specific filament wound composite drive shaft which was supposed to be installed in a cooling tower. Three different composite laminates were optimized by BA to evaluate their final mass to cost ratio. The laminates were Glass fiber reinforced epoxy (GFRE), Carbon fiber reinforced epoxy (CFRE) and a hybrid of them. The conduction of BA led to just one optimum output for GFRE and CFRE;however, a cost-mass diagram including various acceptable solutions was the end result for the hybrid drive shaft. At the end, the BA predictions of the lowest cost optimized hybrid drive shaft were compared with the results of ANSYS simulations.
With the proliferation of graphics processing units (GPU) supporting general-purpose computing (GPGPU), many computationally demanding applications are being redesigned to exploit the capabilities offered by massively...
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ISBN:
(纸本)9783319234373;9783319234366
With the proliferation of graphics processing units (GPU) supporting general-purpose computing (GPGPU), many computationally demanding applications are being redesigned to exploit the capabilities offered by massively parallel computing platforms. This paper presents a bees algorithm (BA) for the Quadratic Assignment Problem (QAP) implemented on the CUDA platform. The motivations for our work were twofold: firstly, we wanted to develop a dedicated algorithm to solve the QAP showing both time and optimization performance, secondly, we planned to check if the capabilities offered by popular GPUs can be exploited to accelerate hard optimization tasks requiring high computational power. The paper describes both sequential and parallel algorithm implementations, as well as reports results of tests.
One of the important steps in the earthquake disaster management is the establishment of temporary relief centers, to provide the basic helps and support in short time. Finding optimum location for these centers with ...
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One of the important steps in the earthquake disaster management is the establishment of temporary relief centers, to provide the basic helps and support in short time. Finding optimum location for these centers with adequate covering of the urban areas is not a trivial problem. The meta-heuristic algorithms are promising methods, capable of solving such complex optimization problems. The goal of this research was to compare the performance of Genetic algorithm (GA) and bees algorithm (BA) in finding the optimum location of relief centers and in allocating of the parcels to them. In order to limit the search space, GIS was used for selecting a few candidate sites that satisfy the initial conditions and criteria. Then, the two algorithms were used to select nine optimum sites among the candidates and to allocate the parcels to them, while minimizing the sum of all distances between centers and parcels. To calibrate the parameters of the algorithms, a simple simulated data set was used. Having proper values for those parameters, the algorithms were tested on the real data of the study area. The results showed that the convergence of the BA was rather gradual, while the trend for GA was relatively stepwise. Both algorithms showed high levels of repeatability. For both the simulated and real data, GA showed to be faster than BA. Simplicity and repeatability of the algorithm are the main factors from the user's point of view. Therefore, considering these criteria, the GA is more favorable than the BA. (C) 2016 Elsevier Ltd. All rights reserved.
Image segmentation is one of the most important tasks in image processing and pattern recognition. One of the most efficient and popular techniques for image segmentation is image thresholding. Among several threshold...
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Image segmentation is one of the most important tasks in image processing and pattern recognition. One of the most efficient and popular techniques for image segmentation is image thresholding. Among several thresholding methods, Kapur's (maximum entropy (ME)) and Otsu's methods have been widely adopted for their simplicity and effectiveness. Although efficient in the case of bi-level thresholding, they are very computationally expensive when extended to multilevel thresholding because they employ an exhaustive search for the optimal thresholds. In this paper, a fast scheme based on a modified bees algorithm (BA) called the Patch-Levy-based bees algorithm (PLBA) is adopted to render Kapur's (ME) and Otsu's methods more practical;this is achieved by accelerating the search for the optimal thresholds in multilevel thresholding. The experimental results demonstrate that the proposed PLBA-based thresholding algorithms are able to converge to the optimal multiple thresholds much faster than their corresponding methods based on Basic BA. The experiments also show that the thresholding algorithms based on BA algorithms outperform corresponding state-of-the-art metaheuristic-based methods that employ Bacterial Foraging Optimization (BFO) and quantum mechanism (quantum-inspired algorithms) and perform better than the non-metaheuristic-based Two-Stage Multi-threshold Otsu method (TSMO) in terms of the segmented image quality. In addition, the results show the high degree of stability of the proposed PLBAbased algorithms. (C) 2016 Elsevier B.V. All rights reserved.
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