Support vector regression and ant colony optimization for HVAC cooling load prediction

Support vector regression with genetic algorithms in forecasting tourism demand

In this paper, the no-wait flow shop scheduling problem under makespan and flowtime criteria is addressed. The no-wait flowshop is a variant of the well-known flowshop scheduling problem where all processes follow the previous one without any interruption for operations of a job. Owing to the problem is known to be NP-hard for more than two machines, a hybrid meta-heuristic algorithm based on ant colony optimization (ACO) and simulated annealing (SA) algorithm is improved. First, at each step, due to the characteristic of ACO algorithm that include solution construction and pheromone trail updating, some different areas of search space are checked and best solution is selected. Then, to enhance the quality and diversity of the solution and finding best neighbor of this solution, a novel SA is presented. Moreover, a new principle is applied for global pheromone update based on the probability function like SA algorithm. The proposed approach solution is compared with several the state-of-the-art algorithms in the literature. The reported results show that the proposed algorithms are effective and the new approach for local search in ACO algorithm is efficient for solving the no-wait flow shop problem. Then, we employed another hybrid ACO algorithm based on hybridization of ACO with variable neighborhood search (VNS) and compare the results given by two proposed algorithms. These results show that our new hybrid provides better results than ACO-VNS algorithm.

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).

The Support Vector Regression (SVR) parameters selection problem is considered in this report. The purpose of work is to create an intelligent algorithm for SVR parameters optimization and, thus, to improve the accuracy of modeling in soft measurements (on example of the rice leaves modeling and data processing). Initially, the program collects the statistical data of learning assemblies analysis results and evaluates the values of SVR parameters, after then it uses developed by the authors improved fruit fly optimization algorithm to get more accurate values of SVR parameters. During the experiments, the obtained results were compared with the results of work of classical Fruit Fly Optimization Algorithm (FOA). It was found, that the authors' algorithm works more effective in convergence speed and accuracy. Thus, it provides the better solution of SVR optimal parameters selection problem.

Forecasting systems reliability based on support vector regression with genetic algorithms

A dynamic particle filter-support vector regression method for reliability prediction

In order to improve the utilization of cloud computing resources, this paper studies cloud computing resource scheduling based on improved Ant Colony Optimization Algorithm (ACO) and Simulated annealing algorithm (SA). We apply ACO and SA to schedule the large-scale cloud computing resource data. This paper uses SA to prevent ACO falling into the local optimal solution, and accelerate the convergence speed of ACO. The experimental results show that the proposed method is more effective compared with the already existed approaches. Our proposed method can improve the resource utilization.

A novel support vector regression(SVR) was proposed for complex grouting process, which has the feature of invisible stratum and varied rock facture width. The features of SVR were selected through analyzing the major mechanism sub-models. In order to improve regression model performance, a fast embedded uniform algorithm of optimizing parameters of SVR was introduced. Two steps of optimization are adopted in the embedded uniform algorithm. Firstly rough parameters of SVR are found by throwing uniform point in rectangle area of parameters, seccondly the precise parameters of SVR are found in new subsection of grid. At last, 600 sets data have be selected to test in HENGYANG dam project of Hunan province in China. The results of simulation show that the embedded uniform method is superior to grid algorithm in arithmetic speed and precision in grouting model.

In this paper, division algebras are proposed as an elegant basis upon which to extend support vector regression (SVR) to multidimensional targets. Using this framework, a multitarget SVR called epsilon(Z)-SVR is proposed based on an epsilon-insensitive loss function that is independent of the coordinate system or basis used. This is developed to dual form in a manner that is analogous to the standard epsilon-SVR. The epsilon(H)-SVR is compared and contrasted with the least-square SVR (LS-SVR), the Clifford SVR (C-SVR), and the multidimensional SVR (M-SVR). Three practical applications are considered: namely, 1) approximation of a complex-valued function; 2) chaotic time-series prediction in 3-D; and 3) communication channel equalization. Results show that the epsilon(H)-SVR performs significantly better than the C-SVR, the LS-SVR, and the M-SVR in terms of mean-squared error, outlier sensitivity, and support vector sparsity.

Forecasting of national expressway network scale is an important part of expressway network planning. It is a complicated problem due to its strong nonlinearity and small quantity of training data. Firstly, An AGA-SVR forecasting model is proposed by combining the support vector regression (SVR) and adaptive genetic algorithm (AGA) in this paper. SVR has been successfully employed to solve regression problems of nonlinearity and problem samples, but selecting appropriate parameters, which is a combinatorial optimization problem, is very crucial to learning performance and generalization performance of SVR. An AGA is used to determine the free parameters of support vector regression. Finally, examples of the scale data of China national expressway network are used to illustrate the performance of the proposed AGA-SVR model. Experimental results demonstrate that the AGA-SVR outperforms the BP neural-network; the AGA-SVR model is a reasonable alternative to forecast national expressway network scale.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution, to get ant colony algorithm's initial pheromone distribution and according to the pheromone content to eliminate some poor path. Secondly, using ant colony algorithm for the optimal path, it was gotten by particle swarm optimization for the secondary search. Finally, get the optimal solution of path search. The simulation results show that, the fusion algorithm does better than the ant colony algorithm in terms of path optimization.

Sediment phenomenon is very important in hydraulic and water resources issues. The existence of this phenomenon causes many problems in water storage. Sediment simulation in rivers helps in controlling sediment as well as reducing damages. In this study, an attempt was made to estimate the suspended sediment load using the corresponding river flow rate in the Zohreh River, Iran using the newest intelligent simulation methods. This study seeks to couple the nonlinear support vector regression (SVR) with crowd intelligence optimization algorithms. For this purpose, support vector regression was optimized using four new crowd optimization algorithms including the ant colony optimizer (ACO), the ant lion optimizer (ALO), the dragonfly algorithm (DA), and the salp swarm algorithm (SSA). Simulation was done in the two phases of train and test. Due to the integration of the nonlinear support vector regression with the optimization algorithms, the model train phase requires more time than usual situations. Therefore, in the current study, taking into account the number of different iterations including 25, 50, 100 and 200 iterations to perform the optimization of the model and tried to find the best optimizer by considering the calculated error and the run time. It was generally found that the SVR model is accurate in estimating the suspended sediment load. Finally, according to the calculated error as well as the run time, the support vector regression model optimized with the salp swarm algorithm with 25 iterations was chosen as the best model. Also, the values of R2, NSE, and RMSE for the best model in the test phase were calculated as 1, 1, and 10.2 tons per day, respectively, and the algorithm run time was 252 s.

The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.