Jaya optimizationalgorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has s...
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ISBN:
(数字)9781665470452
ISBN:
(纸本)9781665470469;9781665470452
Jaya optimizationalgorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has shown great potentials to solve various hard and complex optimization problems, but there still has much room to improve its performance, especially for solving high-dimensional and non-convex problems. Hence, this paper proposes an improved Jaya optimizationalgorithm with a novel hybrid logistic-sine-cosine chaotic map, which is named IJaya for short. The hybrid logistic-sine-cosine chaotic map is applied to balance the exploration and the exploitation processes of Jaya optimizationalgorithm. Seven benchmark testing functions with different scale settings are used to evaluate the performance of our improved algorithm. Computational results indicate that our improved Jaya optimizationalgorithm outperforms greatly its original version on most testing functions with high-dimensions.
In this paper, a mixed H-2/H-1 robust control strategy that considers the weights of the H-2 and H-1 norm in the optimization process is proposed, and it is used to solve the load frequency control (LFC) problem of th...
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In this paper, a mixed H-2/H-1 robust control strategy that considers the weights of the H-2 and H-1 norm in the optimization process is proposed, and it is used to solve the load frequency control (LFC) problem of the micro-grid (MG). The MG load frequency model established in this paper includes battery energy storage system (BESS), fuel cell (FC), wind turbine (WT), photo-voltaic (PV), and diesel engine generator (DEG). The optimal mixed H-2/H-1 robust controller takes the minimum square integral of the system's frequency fluctuation as the goal of control optimization by integrating the robust performance expressed by the H-2/H-1 two norms. The hybrid particle swarm optimization and gravitational search algorithm with chaotic map algorithm (CPSOGSA) is used to optimize the weight value reflecting the H-2 and H-1 performance of the system and the evaluation function's weighting matrix of the output performance so that the controller can reach the optimum under the constraints. Simulation experiments show that the robust controller designed by the proposed method has better dynamic performance when compared with H-1 robust controller, H-2 robust controller, and traditional H-2/H-1 robust controller, and the results are very satisfactory.
Blockchain is well known as a database technology supporting digital currencies such as Bitcoin, Ether and Ripple. For the purpose of maximizing the overall revenue of the blockchain system, we propose a pricing polic...
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Blockchain is well known as a database technology supporting digital currencies such as Bitcoin, Ether and Ripple. For the purpose of maximizing the overall revenue of the blockchain system, we propose a pricing policy to impose on transactions. Regarding the mining process as a vacation, and the block-verification process as a service, we establish a type of non-exhaustive queueing model with a limited batch service and a possible zero-transaction service. By selecting the beginning instant of a block-verification process as a Markov point and using the method of a generating function, we obtain the stationary probability distribution for the number of transactions in the system at the Markov points and analyze the elapsed time for the mining cycle. Based on the model analysis results, we derive the average latency of transactions and demonstrate how the average latency of transactions changes in relation to the arrival rate of transactions. With a reward-cost structure, we construct an individual benefit function and a social benefit function. By improving the Grasshopper optimizationalgorithm (GOA), we search for the Nash equilibrium and the socially optimal arrival rates of transactions. Numerical results show that the Nash equilibrium arrival rate of transactions is always higher than the socially optimal arrival rate of transactions for a given mining parameter and a specific block capacity. For this, we propose a pricing policy that forces the transactions to accept the socially optimal arrival rate and maximize the overall revenue of the blockchain system, including all transactions and miners.
In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regres-sion and particle swarm optimization algorith...
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In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regres-sion and particle swarm optimizationalgorithm. For the proposed method, the global per-formance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objec-tive functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process re-gression and particle swarm optimizationalgorithm was proposed for multi-objective op-timization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint;R a revolute joint;U a universal joint). The comparison from the back propagation neural network, multivariate regres-sion, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimizationalgorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization, the performance i
Ecological conservation is an important objective in urban runoff management today. Maintaining a sustainable ecological system is as equally important a task as to ensure the safety of drainage systems and runoff man...
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Ecological conservation is an important objective in urban runoff management today. Maintaining a sustainable ecological system is as equally important a task as to ensure the safety of drainage systems and runoff management cost control. Thus, the socioecological influences of runoff control infrastructure are innovatively included in a uniform evaluation framework with the control functions and capital investments in this study. These indexes are quantified through hydrological model simulation, life cycle cost analysis, and life cycle assessment. Traditional grey infrastructure and rapidly developing green infrastructure for runoff control are optimized simultaneously. For the trade-off between multiobjectives and configurations of multi-infrastructures, nondominated sorting genetic algorithm-II is utilized to achieve automatic optimization of runoff control infrastructure scale, thereby avoiding the dilemma where manually arranged schemes cannot perform optimally. This multiobjective intelligentoptimization is applied to a sponge city pilot region in Wuhan, China, and tradeoffs are made in the Pareto optimal solution set. A breakthrough is claimed in quantifying the respective contribution of green and grey infrastructures to the optimal scenario in terms of runoff control function, cost input, and socioecological influence. For socioecological influence, the paybacks can meet the investment in the aspect of toxicity health hazard, pathogenic matter, global warming, terrestrial acidification, and water eutrophication (average socioecological paybacks are 2.0, 2.1, 2.9, 1.9, and 2.1 times to the investments respectively). Results prove the necessity of considering multiobjective optimization and green-grey couple infrastructures in a uniform framework.
Heat exchanger networks are important for energy recovery and cost reduction. Flexible networks can handle environmental disturbances, and is more practical for industrial processes than the conventional HEN design wi...
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Heat exchanger networks are important for energy recovery and cost reduction. Flexible networks can handle environmental disturbances, and is more practical for industrial processes than the conventional HEN design with fixed parameters. Previous research on flexible HEN synthesis has mainly applied sequential synthesis, in which the structure determination and area optimization are conducted separately, or other multi-period methods in which only a few extreme operating conditions are selected;however, these methods are not sufficiently accurate for solving nonconvex problems. In this study, a bi-level simultaneous optimization strategy is proposed for the synthesis of flexible HENs under the nominal and all the extreme operating conditions. A binary particle swarm optimization is used in the outer layer to optimize the structural variables, and an Alopex-based evolutionary algorithm is used in the inner layer to determine the heat exchanger areas. Via this method, the result of a convex problem can be directly obtained. Furthermore, to deal with nonconvex problems, a flexibility analysis is conducted to adjust the heat exchanger areas on each critical point. The results of two cases with lower total annual costs than those reported previously are included to demonstrate the efficiency of the proposed method.
Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography. Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which c...
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Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography. Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which controls the embedding position of the message in the cover signal. From another point of view, we implement adaptive steganography by improving the STC coding process (named Adaptive-STC). The parity-check matrix is the key to the encoding and extraction process of STC. In this work, we prove that the average embedding change probability of corresponding elements can be changed by adjusting its submatrix. Following that, we specially design an adaptive parity-check matrix to replace the designed distortion cost to restrict the embedding position. The generation of the adaptive parity-check matrix can be formulated as a multi-constrained integer programming problem in which the width of the submatrix is allocated at a fixed height. To solve this particular problem, we propose a targeted intelligent optimization algorithm (named GOAS) that can adaptively generate the parity-check matrix according to different audio cover. The experimental results show that the proposed method outperforms the state-of-the-art adaptive steganography with reduced embedding changes and improved audio quality while ensuring the ability against steganalysis.
Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new *** is an important component in corn stalks,and it is very...
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Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new *** is an important component in corn stalks,and it is very important to determine its content in corn *** this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was *** order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of *** modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.
An improved particle swarm optimization (PSO) with adaptive weighted delay velocity (PSO-AWDV) is proposed in this paper. A new scheme blending weighted delay velocity is firstly presented for a new PSO with weighted ...
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An improved particle swarm optimization (PSO) with adaptive weighted delay velocity (PSO-AWDV) is proposed in this paper. A new scheme blending weighted delay velocity is firstly presented for a new PSO with weighted delay velocity (PSO-WDV) algorithm. Then, to adaptively update the velocity inertia weight, an adaptive PSO-AWDV algorithm is developed based on the evolutionary state of the particle swarm evaluated via a new estimation method. The newly proposed adaptive PSO-AWDV algorithm is tested based on some famous benchmark functions, which can confirm that the performance of PSO-AWDV is superior to several well-known PSO variants and intelligent optimization algorithms in literature.
As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important...
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As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important prac-tical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algo-rithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary dec
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