Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, t...
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ISBN:
(纸本)9781728104669
Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, the performance of AMO is degraded rapidly when the dimensionality is larger than 30. In order to overcome this shortcoming, an improved animal migration algorithm (IAMO) based on interactive learning behavior is proposed in this paper. First, we introduce an interactive learning behavior that individuals will learn from each other by exchanging information. During the search process, the search step is dynamically adjusted. In this case, the intelligence of IAMO is higher than AMO. Second, a refined search method is used to search around the current solutions, and this method can enhance the search ability of the algorithm. Third, a birth-and-death mechanism is designed to avoid local optimum. The effectiveness of IAMO is verified on 100 dimensional benchmark functions, and the empirical results show that the performance of IAMO is promising.
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node pote...
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ISBN:
(纸本)9789881563897
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node potential as design variables, and equilibrium relation between node potential and sub-circuit current as frame-objective function, dynamic design variables optimization algorithm analysis of arbitrary complicated sinusoidal steady-state circuit network is proposed. Universal program computing sub-circuit current and node potential is completed. Practical examples are computed. Effectiveness and feasibility is verified. A new clue is set up for computing complicated alternating-current circuit network rapidly and precisely.
In this paper, a novel Gravitational Artificial Bee Colony (GABC) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems. In this approach, the gravitational search strateg...
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ISBN:
(纸本)9781538682463
In this paper, a novel Gravitational Artificial Bee Colony (GABC) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems. In this approach, the gravitational search strategy was introduced into the artificial bee colony algorithm, and a gravitational bee colony was established. The gravitational bee could search the global optimal result under the influence of both gravitational force and colony cooperation, which makes the optimization process more effectively and efficiently. Based on GABC algorithm, an intelligent kernel clustering model was established, in which the clustering center and kernel parameters were combined to be the optimal variable, while the clustering index was used as the objective function. GABC was utilized to find the optimal result of the clustering model. The standard testing functions were used to test the proposed algorithm, and GABC showed high accuracy and convergence speed. Then the testing data and fault samples were utilized to test the performance of GABC based clustering model, and its superiority on effectiveness and efficiency was demonstrated.
The path planning of Unmanned Underwater Vehicle (UUV) is a crucial aspect of their operation in underwater environments, Meta-heuristic algorithms are extensively utilized for addressing UUV path planning problems. T...
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ISBN:
(纸本)9798350352573
The path planning of Unmanned Underwater Vehicle (UUV) is a crucial aspect of their operation in underwater environments, Meta-heuristic algorithms are extensively utilized for addressing UUV path planning problems. To address the limitations of the traditional dung beetle optimization algorithm (DBO), including inadequate convergence speed and precision in two-dimensional UUV path planning, and its propensity for local optima, an improved dung beetle optimization algorithm (IDBO) is introduced which employing a suite of refinement strategies. Furthermore, the solution capability of the IDBO is validated through the CEC2017 test suite and two-dimensional raster maps that replicate actual underwater environments. The simulation results demonstrate the robust problem-solving capacity of the IDBO, applicable to both benchmark functions and real-world scenarios, affirming the efficacy of the enhancement strategies in practical applications.
Deep learning techniques have emerged in denoising low-dose computed tomography (CT) images to avoid the potential health risks of high ionizing radiation dose on patients. Although these post-processing methods displ...
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ISBN:
(数字)9781728127828
ISBN:
(纸本)9781728127828
Deep learning techniques have emerged in denoising low-dose computed tomography (CT) images to avoid the potential health risks of high ionizing radiation dose on patients. Although these post-processing methods display high quality denoised images, the denoising performance still has the potential to improve. The primary purpose of this work was to determine and analyze the most effective and efficient hybrid loss function in deep learning (DL)-based denoising network. Objective functions in deep learning algorithms are the main keys for optimizing the parameters of a network and can affect the quality of the denoised image significantly. Hence, this work examined the various combinations of the most common objective functions in CT denoising networks, namely L1 loss, per-pixel loss, perceptual loss, and structural dissimilarity loss. Further, a hyperparameter learning algorithm was also introduced to find the best scalable factors of the loss functions in each hybrid loss function combination. For simplicity, RED-CNN was used in this study to easily demonstrate the performance of the losses during the denoising process. Based on this experiment, the balance between these loss function via the gradient-based optimization algorithm could help in the generalizability prediction of designing future CT denoising networks.
A Mobile Ad hoc NETwork (MANET) is an autonomous system of mobile nodes connected by wireless links. There is no static infrastructure such as base station as that was in cell mobile communication. Due to the dynamic ...
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ISBN:
(纸本)078039335X
A Mobile Ad hoc NETwork (MANET) is an autonomous system of mobile nodes connected by wireless links. There is no static infrastructure such as base station as that was in cell mobile communication. Due to the dynamic nature of the network topology and restricted resources, quality of service (QoS) and multicast routing in MANET is a challenging task. In this paper, we present an entropy-based model to support QoS multicast routing optimization algorithm in mobile ad hoc networks (EQMOA). The basic motivations of the proposed modeling approach stem from the commonality observed in the location uncertainty in mobile ad hoc wireless networks and the concept of entropy. The simulation results demonstrate that the proposed approach and parameters provide an accurate and efficient method of estimating and evaluating the route stability in dynamic mobile networks.
Immune evolutionary algorithm is proposed based on the evolutionary principle in the immune system. In the algorithm, two new parameters of expansion radius and mutation radius are defined to construct a small neighbo...
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ISBN:
(纸本)0780378652
Immune evolutionary algorithm is proposed based on the evolutionary principle in the immune system. In the algorithm, two new parameters of expansion radius and mutation radius are defined to construct a small neighborhood and a large neighborhood. Then expansion and mutation operations are designed to perform local and global search respectively by using the two neighborhoods, thus, two-level neighborhood search mechanism is realized. The results of multi-modal function optimization show that the algorithm has nice global and local searching performances. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due to multivariable inputs, state variable synthesis scheme is suggested to reduce the number of fuzzy rules. Experimental results show that the designed controller can control actual inverted pendulum successfully.
A Mobile Ad hoc NETwork (MANET) is an autonomous system of mobile nodes connected by wireless links There is no static infrastructure such as base station as that was in cell mobile communication. Due to the dynamic n...
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ISBN:
(纸本)0769524052
A Mobile Ad hoc NETwork (MANET) is an autonomous system of mobile nodes connected by wireless links There is no static infrastructure such as base station as that was in cell mobile communication. Due to the dynamic nature of the network topology and restricted resources, quality of service (QoS) and multicast routing in MANET is a challenging task In this paper, we present an entropy-based model to support QoS multicast routing optimization algorithm in mobile ad hoc networks (EQMOA). The basic motivations or the proposed modeling approach stem from the commonality observed in the location uncertainty in mobile ad hoc wireless networks and the concept of entropy. The simulation results demonstrate that the proposed approach and parameters provide an accurate and efficient method of estimating and evaluating the route stability in dynamic mobile networks.
The hurtless location epilepsy foci always adopts the method, which solve EEG or EMG inverse problem with the model of equal current dipole. And the optimization algorithm has much effect on the validity of computing ...
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ISBN:
(纸本)0780372115
The hurtless location epilepsy foci always adopts the method, which solve EEG or EMG inverse problem with the model of equal current dipole. And the optimization algorithm has much effect on the validity of computing result. This paper proposes a kind of genetic algorithms (GAs) aiming at defects of previous optimization algorithms. At the same time, we have located foci of several epileptics by collecting multi -channel EEG. The result shows that GAs has certain practical in hurtless location of epilepsy foci.
A finite-element analysis-based optimal design of an electric machine takes considerable time for its objective evaluation and has many local minima. Thus, selecting an appropriate global convergence optimization with...
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A finite-element analysis-based optimal design of an electric machine takes considerable time for its objective evaluation and has many local minima. Thus, selecting an appropriate global convergence optimization with fast convergence speed is necessary in the optimal design of an electric machine. In this paper, a novel global search optimization algorithm, mass ionized particle optimization (MIPO), is newly proposed. The MIPO is the population-based algorithm, which reflects the interactive force between the ionized particles. The global convergence and the convergence speed are validated by comparison with the particle swarm optimization, which have already been proved for its global convergence when applied to a well-known Goldstein-Price function as a benchmark function. In addition, the algorithm has been applied to the optimal design of an interior permanent magnet synchronous machine aiming for its torque ripple reduction.
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