To cope with the approaching POST-2020 scenario, the national CO2 emission in the building sector, which accounts for 25.5% of the total CO2 emissions, should be managed effectively and efficiently. To do this, it is ...
详细信息
To cope with the approaching POST-2020 scenario, the national CO2 emission in the building sector, which accounts for 25.5% of the total CO2 emissions, should be managed effectively and efficiently. To do this, it is essential to forecast the national CO2 emissions in the building sector by region. As the South Korean government does not currently do this by region, regional characteristics are rarely taken into consideration when managing the national CO2 emissions in the building sector. Towards this end, this study developed an optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms. Compared to the forecasting performance of the gene expression programming model, the forecasting performance of the optimized gene expression programming harmony search optimization model has improved by 7.11, 2.05, and 2.06% in terms of the mean absolute error, root mean square error, and mean absolute percentage error, respectively. Various national CO2 emissions scenarios in the building sector were established in order to better analyze the variation range of the national CO2 emissions in the building sector. Compared to the national CO2 emissions in 2016 (i.e., scenario 1: 41,337 ktCO(2);scenario 2: 45,373 ktCO(2);scenario 3: 46,024 ktCO(2)) in multi-family housing complexes, the national CO2 emissions in 2030 (i.e., scenario 1: 37,579 ktCO(2);scenario 2: 37,736 ktCO(2);scenario 3: 37,754 ktCO(2)) in multi-family housing complexes are forecasted to increase by 10.00-21.91%. The developed optimized gene expression programming harmony search optimization model will potentially be able to assist policymakers in central and local governments forecast the national CO2 emissions in 2030. Through this, national CO2 emission management that more closely reflects the characteristics at the regional or national level can be supported.
We study a partner selection problem for virtual manufacturing enterprises facing an uncertain environment. We propose a new method of using the grey system theory to account for uncertainties in a project's start...
详细信息
We study a partner selection problem for virtual manufacturing enterprises facing an uncertain environment. We propose a new method of using the grey system theory to account for uncertainties in a project's start time, completion time, transportation time, as well as cost. We first develop the concepts of delivery time agreement index and cost agreement index, and then formulate a multi-objective partner selection problem that maximizes the minimum delivery time agreement index, the average delivery time agreement index and the cost agreement index. A chaotic particleswarmoptimization (CPSO) algorithm is developed. Extensive computational results show that the proposed CPSO method outperforms the standard PSO in providing quality solutions more reliably.
In this paper, we present a novel wavefront sensing method for diffraction optical system based on phase diversity. Based on the physical-imaging mechanism of diffractive optical system, the wavefront characteristics ...
详细信息
In this paper, we present a novel wavefront sensing method for diffraction optical system based on phase diversity. Based on the physical-imaging mechanism of diffractive optical system, the wavefront characteristics of the diffraction optical system are characterized by using diffraction efficiency. On this basis, a novel modified phase diversity (PD) wavefront sensing method is established based on the blocking idea. After that, the global optimization method of corresponding method based on particle swarm optimization algorithm is proposed. Finally, some experiment results are achieved with the experiment on a membrane diffraction optical system. Experimental results indicate that the proposed algorithm performs well on both wavefront reconstruction and image restoration and is far superior to traditional methods in diffraction optical systems. The accuracy of our proposed PD method is at least two orders of magnitude higher than the traditional method. Taking the 0.01 degrees field of view for example, our modified PD method can achieve the accuracy of 2.5 x 10(-4) wavelength when the diffraction efficiency is higher than 0.6. This proposed method can be applied to improve the image quality of the diffraction optical system and further support the on-orbit application of ultra-large aperture membrane imaging technology.
An order scheduling problem arises in numerous production scheduling environments. Makespan, mean flow time, and mean tardiness are the most commonly discussed and studied measurable criteria in the research community...
详细信息
An order scheduling problem arises in numerous production scheduling environments. Makespan, mean flow time, and mean tardiness are the most commonly discussed and studied measurable criteria in the research community. Although the order scheduling model with a single objective has been widely studied, it is at odds with real-life scheduling practices. In practice, a typical manager must optimize multiple objectives. A search of the literature revealed that no articles had addressed the issue of optimizing an order scheduling problem with multiple objectives. Therefore, an order scheduling model to minimize the linear sum of the total flowtime and the maximum tardiness is introduced in this study. Specifically, several dominance relations and a lower bound are derived to expedite the search for the optimal solution. Three modified heuristics are proposed for finding near-optimal solutions. A hybrid iterated greedy algorithm and a particleswarm colony algorithm are proposed to solve this problem. Finally, a computational experiment is conducted to evaluate the performances of all proposed algorithms.
The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating i...
详细信息
The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly algorithm, Flower Pollination algorithm and particle swarm optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems. (C) 2017 Society for Computational Design and Engineering. Publishing Services by Elsevier.
With the development of smart grid, the demand for voltage measurement along the overhead transmission lines is increasing. However, the installation of voltage transformers in the existing lines entails numerous diff...
详细信息
With the development of smart grid, the demand for voltage measurement along the overhead transmission lines is increasing. However, the installation of voltage transformers in the existing lines entails numerous difficulties. This study proposes a new non-contact measurement method by inversely calculating the voltages on AC overhead transmission lines based on the power-frequency electric field measurement data. To improve the calculation accuracy, the 3D model of transmission lines is built, and the relations between the 3D electric fields and voltages are presented. An improved algorithm that intermingles with the particleswarmalgorithm and genetic algorithm is developed to ascertain optimal inverse solutions, meanwhile to improve the convergence speed and calculation stability. To further reduce the computational complexity, the constraint relations between the voltages and electric fields are derived, thereby the simplification from three decision variables to one is achieved. Then, some simulation cases with different measurement errors and voltage running states are conducted to show the good robustness and high accuracy of the proposed inversion method. Finally, a three-phase experimental system is built and the actual measured data are used to inversely calculate, which verifies the practicability of the proposed non-contact voltage measurement method.
Wireless location network combines data communication,contextual data collection and *** purpose of this network is to determine the positions of agents based on measurements between nodes and *** order to improve the...
详细信息
Wireless location network combines data communication,contextual data collection and *** purpose of this network is to determine the positions of agents based on measurements between nodes and *** order to improve the positioning accuracy of wireless location network,the usual method is to increase the density of nodes,especially the density of anchor nodes,so as to optimize the topology of the ***,the wireless location network node power is limited,especially the wireless sensor *** is necessary to increase power consumption to meet the needs of a large number of ***,it is required that a certain level of power consumption should be set in the wireless location network,and even require further reduction in power consumption in daily *** the same time,we need to maintain a certain positioning accuracy,which requires the optimization of power allocation in the network,to make sure that power and positioning accuracy of the network can meet the needs of the actual *** this paper,an optimization of power allocation based on particleswarmoptimization in wireless location network is proposed to optimize the power *** square position error bound is introduced as the evaluation standard of the network positioning *** the power optimization,the positioning accuracy of the network is improved.
Blind deconvolution is a method for enhancing the fault feature of rolling element bearings. Based on different maximization criteria, including kurtosis, correlated kurtosis, D-norm, multi-D-norm, and cyclostationari...
详细信息
Blind deconvolution is a method for enhancing the fault feature of rolling element bearings. Based on different maximization criteria, including kurtosis, correlated kurtosis, D-norm, multi-D-norm, and cyclostationarity indicator, different blind deconvolution algorithms have been proposed as powerful tools for fault feature extraction. However, kurtosis and D-norm are susceptible to extreme values, while the other three criteria strongly rely on prior knowledge of the fault period. To overcome the shortcomings of the existing criteria, this study proposes a new criterion called impulse-norm. It is a time-domain parameter defined as the ratio of the average amplitude of the first several maximum energy points to the energy of the entire signal. As opposed to kurtosis and D-norm, the impulse-norm is not affected by strong random impulses. Unlike correlation kurtosis, multi-D-norm and cyclostationarity indicator, it is also independent from the fault period. Based on impulse-norm, we also propose a new deconvolution algorithm called particleswarmoptimization-based maximum impulse-norm deconvolution. This blind deconvolution algorithm employs generalized sphere coordinate transformation and adopts the PSO algorithm to optimally solve the filter coefficients by maximizing the impulse-norm of the signal being filtered. The proposed method was validated using simulated signals and high-speed train axle-box bearing experimental signals. The simulation and experimental results indicated that the proposed PSO-MIND method can effectively identify the weak impulse fault feature of rolling element bearings. (C) 2019 Elsevier Ltd. All rights reserved.
U-shaped steel damper (USSD), as an energy dissipation device, has been recommended in the literature for using in the isolation systems. This type of damper is capable of appropriately dissipating the input energy wh...
详细信息
U-shaped steel damper (USSD), as an energy dissipation device, has been recommended in the literature for using in the isolation systems. This type of damper is capable of appropriately dissipating the input energy which a structure imparts from an earthquake. The capability is provided through a large range of plastic deformations occurred in the USSD. This paper aimed at presenting a methodology structured in the framework of a shape optimization problem to enhance the seismic energy dissipation and deformation capability of the USSD under cyclic loading. To achieve this goal, the straight part, thickness and height of the USSD were considered as the design variables of the optimization problem and optimized through maximizing the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain. In order to find the optimum shape of the USSD under cyclic loading, a hybrid approach consisted of two phases was applied. In the first phase, as an alternative for the time-consuming finite element analysis, a support vector machine (SVM) approach was trained, tested and used to predict the inelastic responses of the USSD. In the second phase, a modified particleswarmoptimization (PSO) algorithm was adopted to find the optimum shape of the USSD subjected to two critical directions of cyclic loading. After finding the optimum shape of the USSD, the energy dissipation and deformation capability of the optimum shaped-USSD were assessed. Results demonstrate that the proposed shape optimization methodology renders an optimum-shaped USSD with significantly improved energy dissipation and deformation capability compared with those of available in the literature.
To solve the low accuracy, slow convergence and poor robustness problem of traditional neural network method for water quality forecasting, a new model of dissolved oxygen content prediction is proposed based on slidi...
详细信息
暂无评论