We propose an artificial bee colony algorithm for the problem of composing medical crews with equity and efficiency principles. The objective is to constitute crews of practitioners with different skills and as effici...
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We propose an artificial bee colony algorithm for the problem of composing medical crews with equity and efficiency principles. The objective is to constitute crews of practitioners with different skills and as efficient as possible. The artificial bee colony algorithm is a swarm intelligence model inspired in the foraging behaviour of honeybees. In this framework, bees produce candidate solutions for the problem by exploring the vicinity of food sources. The proposed approach exploits useful knowledge of the problem at this neighbourhood exploration, considering the partial destruction and heuristical reconstruction of selected solutions. We show the effectiveness of our model through an empirical analysis where three different contexts are considered: easy, challenging, and difficult problem cases. The results are compared with those of a genetic algorithm and the current state-of-the-art method for this problem. (C) 2015 Elsevier Inc. All rights reserved.
This paper describes an application of a hybrid of fuzzy logic (FL) and multiobjective artificial bee colony algorithm (MOABC) for optimizing the torch brazing process of aluminum in the fabrication of condensers in t...
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This paper describes an application of a hybrid of fuzzy logic (FL) and multiobjective artificial bee colony algorithm (MOABC) for optimizing the torch brazing process of aluminum in the fabrication of condensers in the automotive manufacturing industry of Juarez, Mexico. This work aims to show how artificial intelligence is being applied in the manufacturing sector of Mexico for optimizing processes leading to cost reduction. The approach consists of using FL as surrogate model of the brazing process;after, MOABC is applied to find the nondominated solutions for leak rate which is a quality test of the condenser and production time. Results show the use of artificial intelligence is an excellent tool for optimizing manufacturing processes leading to improve productivity, mainly in the selected region, where this type of methodologies are fairly new in applicability.
Probabilistic stability analysis is an effective way to take uncertainties into account in evaluating the stability of slopes. This paper presents an intelligent response surface method for system probabilistic stabil...
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Probabilistic stability analysis is an effective way to take uncertainties into account in evaluating the stability of slopes. This paper presents an intelligent response surface method for system probabilistic stability evaluation of soil slopes. artificial bee colony algorithm (ABC) optimized support vector regression (SVR) is used to establish the response surface to approximate the limit-state function. Then Monte Carlo simulation is performed via the ABC-SVR response surface to estimate system failure probability. The proposed methodology is verified in three case examples and is also compared with some well-known or recent algorithms for the problem. Results show that the new approach is promising in terms of accuracy and efficiency. (C) 2015 American Society of Civil Engineers.
artificialbeecolony (ABC) algorithm is a nature-inspired metaheuristic based on imitating the foraging behaviour of bee, which is widely used in solving complex multi-dimensional optimisation problems. In order to o...
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artificialbeecolony (ABC) algorithm is a nature-inspired metaheuristic based on imitating the foraging behaviour of bee, which is widely used in solving complex multi-dimensional optimisation problems. In order to overcome the drawbacks of standard ABC, such as slow convergence and low solution accuracy, we propose an improved multi-strategy artificial bee colony algorithm (MSABC). According to the type of position information in ABC, three basic search mechanisms are summarised, the mechanisms include searching around the individual, the random neighbour and the global best solution. Then, the basic search mechanisms are improved to obtain three search strategies. Each bee randomly selects a search strategy to produce a candidate solution under the same probability in each iteration. Thus these strategies can make a good balance between exploration and exploitation. Finally, the experiments are conducted on eight classical functions. Results show that our algorithm performs significantly better than several recently proposed similar algorithms in terms of the convergence speed and solution accuracy.
In order to o ercome the shortcomings of artificial bee colony algorithm including slow convergence speed,easily falling into local optimum value, neglect of development and other issues, Mechanism of other bionic int...
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In order to o ercome the shortcomings of artificial bee colony algorithm including slow convergence speed,easily falling into local optimum value, neglect of development and other issues, Mechanism of other bionic intelligent optimization algorithms, A new algorithm of Global artificial bee colony algorithm based on crossover which can effectively improve the convergence rate, enhance the development of the algorithm and the global optimization ability is proposed, and the algorithm can effectively avoid the local optimum. Finally, the Seven standard test functions are selected to carry out the experiment and simulation. The results show that the convergence speed and accuracy of the pro posed algorithm（CGABC） are significantly improved compa red with other algorithms such as ABC algorithm, GABC algorithm and so on.
The Deep Belief network has become a powerful tool in nowadays to large-scale-oriented application,however,there are several parameters need to assign in advances that is key factors of successive *** this paper,we pr...
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The Deep Belief network has become a powerful tool in nowadays to large-scale-oriented application,however,there are several parameters need to assign in advances that is key factors of successive *** this paper,we proposed to address the issue of properly fine-tuning parameters of Deep Belief Networks by means of artificialbeecolony（ABC） *** results show that the proposed ABC algorithm can effectively reconstruct the original binary images.
This paper exposes an Optimal Power Flow (OPF) problem resolution using a recent developed meta-heuristic algorithm called artificialbeecolony (ABC) algorithm based Grenade Explosion Method (GEM). From many previous...
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This paper exposes an Optimal Power Flow (OPF) problem resolution using a recent developed meta-heuristic algorithm called artificialbeecolony (ABC) algorithm based Grenade Explosion Method (GEM). From many previous researches, the ABC algorithm has proved its goodness at exploration process in the search space for better solutions, but it is weak at exploitation process. The proposed GEM associated with the ABC algorithm gives more ability to enhance the exploitation process by proposing two modified versions of basic ABC algorithm, which are GABC1 by embedding GEM in the employed bee phase and GABC2 by introducing GEM in the onlooker bee phase. The effectiveness of the two proposed algorithms in the present work is investigated by solving the optimization problems of objective functions for smooth, non-smooth and piecewise quadratic curves of fuel cost function, along with the minimization of voltage magnitude deviation and voltage stability index. The simulation results on IEEE 30 bus and IEEE 57 bus test systems are compared to those of other optimization techniques in literature, showing that the two proposed algorithms are capable to give higher quality solutions efficiently for a complex OPF problem.
One of the key problems in the field of bioinformatics is protein structure prediction. The thermodynamic hypothesis demonstrates that protein's energy is the lowest in nature state. So protein's structure can...
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ISBN:
(纸本)9781450348713
One of the key problems in the field of bioinformatics is protein structure prediction. The thermodynamic hypothesis demonstrates that protein's energy is the lowest in nature state. So protein's structure can be gotten directly by protein sequence's free-energy. In this paper, an improved algorithm based on three-dimensional AB off-lattice model to improve local search and global optimization ability of artificial bee colony algorithm has been presented. The simulation experiment shows that it can effectively search the lowest free-energy in the condition of keeping high accuracy. The experimental results indicate that the minimum energy from the improved artificial bee colony algorithm is better than other similar algorithms, and with the increase of protein sequence's length, this algorithm has better performance.
This paper proposes a new approach for edge detection using a combination of artificialbeecolony (ABC) algorithm and an improved derivative technique. ABC algorithm simulates the foraging behavior of a honey bee swa...
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This paper proposes a new approach for edge detection using a combination of artificialbeecolony (ABC) algorithm and an improved derivative technique. ABC algorithm simulates the foraging behavior of a honey bee swarm. The proposed approach find the edge pixels by an improved derivative technique to compute fitness function, and then applying ABC algorithm determine the most fit pixels to be considered as the edge pixels. Qualitative and quantitative analysis of the proposed approach and its comparison with other standard edge detection methods are presented. Shannon's entropy function and Pratt's figure of merit are used for quantitative analysis. The effect of variation of parameters on performance of the proposed approach is discussed. Experimental results show that proposed method outperformed most of existing techniques.
In the medical field, it is very important for doctors to make effective and correct decision-making. In order to improve the accuracy of doctors' diagnosis and avoid the misdiagnosis of doctors' intuition, su...
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In the medical field, it is very important for doctors to make effective and correct decision-making. In order to improve the accuracy of doctors' diagnosis and avoid the misdiagnosis of doctors' intuition, subconscious and incomplete knowledge. ABC-NB algorithm is used in the field of chronic disease diagnosis to improve the diagnostic efficiency and reduce the chance of misjudgment. The artificial bee colony algorithm based on improved scale factor is applied to the selection of chronic disease characteristics, and the data are dimensioned, the redundant and irrelevant features are removed, the convergence speed is improved, and the algorithm is applied to search the global optimal solution. Then, the eigenvalues of the pre-processed data are trained and learned to generate the Bayesian classifier to construct the prediction model. The prediction module displays the diagnostic results for medical staff to assist in the diagnosis and decision making. Experiments show that the model has good flexibility and robustness, can have a stable calculation of the probability of diagnosis of chronic diseases, and it is effective for the diagnosis of medical staff.
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