Grey Prediction Evolution Algorithm Based on Accelerated Even Grey Model

Abstract

The grey prediction evolution algorithm based on the even grey model (GPEAe) is a pioneer of prediction-based evolutionary algorithms. Its offsprings are generated by a first-order inverse accumulating generation operation (1-IAGO) depending on every prediction of the even grey model. For the fact that the original values are already known for the first few predicted values in 1-IAGO, this paper firstly develops an accelerated 1-IAGO (1-AIAGO) which replaces a particular prediction with the corresponding original value. An accelerated even grey model (AEGM(1,1)) based on the 1-AIAGO is then proposed. Finally, this paper proposes a new grey prediction evolution algorithm (GPEAae) which uses the AEGM(1,1) as the reproduction operator of evolutionary algorithm to forecast the offspring. The performance of GPEAae is verified on the CEC2014 benchmark functions and the set containing nine engineering constrained design problems. The experimental results show that the GPEAae has superiority and highly competitive effectiveness when compared with the GPEAe and other state-of-the-art algorithms. The motivation of the GPEAae is using the iterative frame of evolutionary algorithms to superimpose the weak effect generated by the proposed 1-AIAGO which replaces the approximate (predicted) value with the corresponding accurate (original) value.

A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

“…This issue may cause excess migrations or invalid migrations, resulting in increases in energy consumption and migration costs. To solve this problem, we propose a gray prediction algorithm with residual correction to predict the loads of hosts in the future [19], [42]. The gray prediction algorithm is an exponential prediction model that uses a small amount of incomplete information to establish a mathematical model and make predictions.…”

“…By calculating the fitness function, determine the individual optimal position and the global optimal position of the initial particle swarm (lines 3-9). In the algorithm process, the velocity and position of each particle at each iteration are updated through (35) and (36), a new VM placement plan is generated, and the corresponding fitness function value is calculated (lines [11][12][13][14][15][16][17][18][19][20][21]. For a plan with a fitness value lower than the average value, the placement plan needs to be updated through a mutation operation to ensure the diversity of the placement plan (lines 23-28).…”

“…In this paper, a gray prediction algorithm based on residual correction is used for load prediction. This algorithm can establish a nonlinear prediction model based on historical load data [19], and the prediction accuracy is higher than that of linear models [8], [20]. Traditional consolidation algorithms [6], [15] are often based on greedy models, and the algorithm complexity is low; however, it is difficult to obtain the global optimal solution with these models.…”

An Improved Grey Prediction Evolution Algorithm for Real-world Engineering Optimization Problems

“…To further demonstrate the performance of the GPEde on 19 benchmark constrained problems from the CEC 2020, algorithms of DE [7], GPE [20] as well as GPE variant algorithms including GPEAae [47], TOBLGPE [30], GPEAed [27] are selected as comparison algorithms. Table 2 shows the experimental results of GPEde in terms of the four indexes of the worst (Worst), mean (Mean), best (Best) and standard deviation (Std) on the 19 functions.…”

Current control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

“…To improve the accuracy of grey prediction, the Lagrange mean theorem is used to construct background values, and the initial value is used as a variable to determine the corresponding optimal parameters and time response formula based on the minimum value of the average relative error [15]. In [16], an accelerated first-order inverse accumulation generation method is proposed based on an even number grey prediction model to optimize the propagation operator to improve prediction accuracy. In [17], based on the new information priority principle, fractional order cumulative grey model is used to establish a prediction model, and particle swarm optimization combined with genetic algorithm is applied to determine the optimal order to achieve the prediction of network security forms.…”