On the Universality of Axon P Systems Xingyi Zhang; Linqiang Pan; Paun, Andrei
IEEE transaction on neural networks and learning systems,
11/2015, Volume:
26, Issue:
11
Journal Article
Axon P systems are computing models with a linear structure in the sense that all nodes (i.e., computing units) are arranged one by one along the axon. Such models have a good biological motivation: ...an axon in a nervous system is a complex information processor of impulse signals. Because the structure of axon P systems is linear, the computational power of such systems has been proved to be greatly restricted; in particular, axon P systems are not universal as language generators. It remains open whether axon P systems are universal as number generators. In this paper, we prove that axon P systems are universal as both number generators and function computing devices, and investigate the number of nodes needed to construct a universal axon P system. It is proved that four nodes (respectively, nine nodes) are enough for axon P systems to achieve universality as number generators (respectively, function computing devices). These results illustrate that the simple linear structure is enough for axon P systems to achieve a desired computational power.
Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that ...can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.
Evolutionary algorithms have been shown to be powerful for solving multiobjective optimization problems, in which nondominated sorting is a widely adopted technique in selection. This technique, ...however, can be computationally expensive, especially when the number of individuals in the population becomes large. This is mainly because in most existing nondominated sorting algorithms, a solution needs to be compared with all other solutions before it can be assigned to a front. In this paper we propose a novel, computationally efficient approach to nondominated sorting, termed efficient nondominated sort (ENS). In ENS, a solution to be assigned to a front needs to be compared only with those that have already been assigned to a front, thereby avoiding many unnecessary dominance comparisons. Based on this new approach, two nondominated sorting algorithms have been suggested. Both theoretical analysis and empirical results show that the ENS-based sorting algorithms are computationally more efficient than the state-of-the-art nondominated sorting methods.
Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set ...of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms.
Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most ...existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.
In this paper, we propose a framework to accelerate the computational efficiency of evolutionary algorithms on large-scale multiobjective optimization. The main idea is to track the Pareto optimal ...set (PS) directly via problem reformulation. To begin with, the algorithm obtains a set of reference directions in the decision space and associates them with a set of weight variables for locating the PS. Afterwards, the original large-scale multiobjective optimization problem is reformulated into a low-dimensional single-objective optimization problem. In the reformulated problem, the decision space is reconstructed by the weight variables and the objective space is reduced by an indicator function. Thanks to the low dimensionality of the weight variables and reduced objective space, a set of quasi-optimal solutions can be obtained efficiently. Finally, a multiobjective evolutionary algorithm is used to spread the quasi-optimal solutions over the approximate Pareto optimal front evenly. Experiments have been conducted on a variety of large-scale multiobjective problems with up to 5000 decision variables. Four different types of representative algorithms are embedded into the proposed framework and compared with their original versions, respectively. Furthermore, the proposed framework has been compared with two state-of-the-art algorithms for large-scale multiobjective optimization. The experimental results have demonstrated the significant improvement benefited from the framework in terms of its performance and computational efficiency in large-scale multiobjective optimization.
Abstract
The second generation HTS wires have been used in many superconducting components of electrical engineering after they were fabricated. New challenge what we face to is how the damages occur ...in such wires with multi-layer structure under both mechanical and extreme environment, which also dominates their quality. In this work, a macroscale technique combined a real-time magneto-optical imaging with a cryogenic uniaxial-tensile loading system was established to investigate the damage behavior accompanied with magnetic flux evolution. Under a low speed of tensile strain, it was found that the local magnetic flux moves gradually to form intermittent multi-stack spindle penetrations, which corresponds to the cracks initiated from substrate and extend along both tape thickness and width directions, where the amorphous phases at the tip of cracks were also observed. The obtained results reveal the mechanism of damage formation and provide a potential orientation for improving mechanical quality of these wires.
Wind farms are enormous and complex control systems. It is challenging and valuable to control and optimize wind farms. Their applications are widely used in various industries. Artificial ...intelligent algorithms are effective methods for optimization problems due to their distinctive characteristics. They have been successfully applied to wind farms. In this paper, several issues in wind farms are presented. Applications of artificial intelligent algorithms in wind farm controllers, Mach number, wind speed prediction, wind power prediction and other problems of wind farms are reviewed. Two future research directions are pointed out to develop artificial intelligent algorithms for wind farm control systems and wind speed and power prediction.
Major depressive disorder is a serious mental disorder that profoundly affects an
individual's quality of life. Although the aetiologies underlying this disorder remain unclear, an
increasing ...attention has been focused on the influence imposed by psychological stress over
depression. Despite limited animal models of psychological stress, significant progress has been
made as to be explicated in this review to elucidate the physiopathology underlying depression and
to treat depressive symptoms. Therefore, we will review classical models along with new methods
that will enrich our knowledge of this disorder.
•Delamination is analyzed using a coupled thermal–mechanical cohesive zone model.•Cohesive strength distribution has a remarkable impact on the delamination.•Influences of thermal-conductivity ...degradation caused by delamination are studied.
Epoxy-impregnated pancake coil is observed to delaminate locally when its temperature is cooled from room temperature to 77 K. The delaminations cause the degradation of the critical current of the coil. Besides, the delamination may degrade the thermal conductivity and affect the thermal conductive path. Thus, in this study, the delamination behavior of epoxy-impregnated pancake coil during the cooling process is analyzed through a coupled thermal–mechanical cohesive zone model. The measured transverse tensile strength of coated conductors has shown considerable scatter and Weibull distribution has been adopted to fit the transverse tensile strength. The simulations are able to capture the main characterizations of the observed delaminations with the cohesive strength of the cohesive element following a Weibull distribution. After the cooling process, a heat spot is applied to the degraded positions to analyze the temperature field and the thermal conductive path in the damaged coil. Compared to the original undamaged coil, the delaminations indeed degrade the thermal conductivity and increase the thermal resistance. What’s more, the effects of the variation of the thickness of the epoxy and different interfacial cohesive strength distributions of the coated conductor are considered. The results show that reduction of the thickness of the epoxy can lower the radial stress and release the damage, and the random distribution of the cohesive strength inside the coated conductor dominantly determines the delamination pattern of the coil.
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