A novel evolutionary algorithm based on even difference grey model

In the HDFS cloud storage environment, the existing replica strategy is not able to adopt the optimal replica according to the current system environment dynamically, which affects the performance of the Cloud Storage system. This paper aims at the replica selection strategies in the load balancing problem, proposes a Replica Strategy based on grey prediction theory in Cloud Storage and predicts the node load rate of different replicas at a moment, selecting the minimum load node dynamically, eventually to load balance.

Three-dimensional (3D) path planning for unmanned aerial vehicles (UAVs) and real-world engineering design problems has been a hot research issue. Traditional methods show limitations in dealing with these complex nonlinear models. To overcome these challenges, this paper proposes a new metaheuristic method, Euler optimizer (ELO), inspired by Euler’s method for the numerical solution of differential equations.ELO simulates Euler’s method to predict the position of the next point by the derivative and step size of the current point to perform the position updating, and sets the exploration weighting factor and the exploration factors that are used to tune the exploration and exploitation capabilities of the algorithm. We compared ELO with 11 representative state-of-the-art algorithms using the CEC2017 and CEC2022 benchmark suites, and performed the Wilcoxon rank sum test and Friedman’s test. The results show that ELO outperforms the other comparative algorithms by 90%, 96%, 99%, and 84% on CEC2017 (30/50/100 dimensions) and CEC2022 (20 dimensions), respectively. Finally, ELO is applied to UAV 3D path planning and five real-world engineering design problems. The experimental results show that ELO achieves the best compared to all 11 comparison algorithms, demonstrating ELO’s effectiveness and extensiveness in engineering optimization problems.

Revealed the strength variation of concrete C30 and C50 after the cold in the initial .Using the gray prediction theory which from the cybernetics to analyze the test results after the initial cold.The results show that the results obtained by the formula is consistent with the experimental results, the residuals are within the control range.The experimental in cold for 120 days is much closer than theoretical values when compared with the residual for 90 days.Research shows that with the increase of initial freezing time, the loss rate of concrete strengthincreased.And the residuals showed a significant increasing trend when the number of freeze-thaw cycles increased.

Based on the statistical data during the period from 2001 to 2004 released by Henan statistical bureau in China, this paper aims to predict the amount of scientists and technicians of Henan province in China by the usage of GM (1, 1) model with the four items. The result of this etechnological policiesmpirical study is that the grey prediction theory can fit the scientists and technicians amount development precisely in Henan province. The accuracy of the prediction result is above 90% and corresponds with a distinction, which the grey theory can meet expectations with small samples or data. Research results show that this model established in this paper could provide valuable information for policy makers in their efforts to make appropriate technological policies.

In case of a carbonate reservoir, conventional techniques such as artificial neural networks and pattern recognition may not perform well in predicting the reservoir potential zones. This paper proposes a new implementation to highlight the anomalous zones of a carbonate reservoir using grey theory. The input is a 3D seismic volume for a square area which contains eight drilled wells of varying producing quality. The grey analysis is carried out for the zone of interest on a trace-by-trace basis. The result is validated on the reservoir main producing layer by utilizing the wells production classification information.

university is a complicated work so in the implementation it have violation of the constraints and it also takes a lot of time since it is created manually. In this paper evolutionary algorithm (EA) is used to create an effective and feasible schedules based on the real data input that is obtained from each department. The objective functions in EA contribute in gaining the fitness function to solve the constraints problem in the schedule by applying weighting for each hard constraints. The objective function is gained from the total of infringement in each soft constraints addition by score weighting. The genetic operator used in EA is stochastic variation Operator. As far as the reproduction operator is concerned, the tournament selection was used with size 3. Crossover operator is conducted after selection process with crossover probability equal to 0.05 and mutation rate is 0.1. The size of population was set to 9 and stopping criteria algorithm was left run for fitness value = 1. The simulation result shows that EA can create lecture schedules efficiently and feasibly. Moreover, it is also faster with the execution time of the proposed EA is less than 30 and easier than creating manually.

We consider the capacity problem in large wireless multihop networks by separating the problem into macroscopic and microscopic level subproblems. At the macroscopic level, the task is to determine the routes that are geometric curves. At the microscopic level, we need to forward the traffic according to its directional distribution that results from the macroscopic level routes. Previous studies have treated the macroscopic level problem simply as that of load balancing, implying the use of time sharing to serve the traffic flowing in different directions. We study how a scheduling that truly interleaves the traffic flows in different directions affects the macroscopic level problem. We restrict the macroscopic routes to certain simple path sets. This together with earlier results on the microscopic level forwarding capacity allows us to obtain new results on the macroscopic level capacity that demonstrate the gains from multidirectional forwarding.

The aim of multimodal optimization is to locate multiple optima of a given problem. Evolutionary algorithms (EAs) are one of the most promising candidates for multimodal optimization. However, due to the use of greedy selection operators, the population of an EA will generally converge to one region of attraction. By incorporating a well-designed selection operator that can facilitate the formation of different species, EAs will be able to allow multiple convergence. Following this research avenue, we propose a novel selection operator, namely, queueing selection (QS) and integrate it with one of the most promising DE variants, called composite differential evolution (CoDE). The integrated algorithm (denoted by CoDE-QS) inherits the strong global search ability of CoDE and is capable of finding and maintaining multiple optima. It has been tested on the CEC2013 benchmark functions. Experimental results show that CoDE-QS is very competitive.

The concept of density is difficult for students to understand because it cannot be directly observed. To gain a deeper understanding of this concept, students need to learn about density at the microscopic and macroscopic levels. Virtual Reality (VR) provides an opportunity for students to experience the phenomena at both levels. A density exploration simulation was used in a Cave Automatic Virtual Environment (CAVE) to investigate the impact of the CAVE on preservice teachers' understanding of density. Three preservice teachers were interviewed before and after their CAVE experience on the concept of density. The data collected consisted of participants' recorded audio responses, written texts, and drawings made during the interviews. Thematic analysis was used to examine the raw data, and three major themes emerged regarding participants' understanding of the concept of density. The experience helped participants visualize water and oil molecules, recognize differences in molecular structures, explore the role of intermolecular forces in determining water density, and observe that air does not affect ice density at the microscopic scale. The study suggests that students can benefit from VR experiences such as those in the CAVE to explore density at macroscopic and microscopic levels.

Gray predicting theory and application of energy consumption of building heat-moisture system

For problems involving uncertainties in design variables and parameters, a bi-objective evolutionary algorithm (EA) based approach to design optimization using evidence theory is proposed and implemented in this paper. In addition to a functional objective, a plausibility measure of failure of constraint satisfaction is minimized. Despite some interests in classical optimization literature, this is the first attempt to use evidence theory with an EA. Due to EA's flexibility in modifying its operators, nonrequirement of any gradient, its ability to handle multiple conflicting objectives, and ease of parallelization, evidence-based design optimization using an EA is promising. Results on a test problem and two engineering design problems show that the modified evolutionary multi-objective optimization algorithm is capable of finding a widely distributed trade-off frontier showing different optimal solutions corresponding to different levels of plausibility failure limits. Furthermore, a single-objective evidence-based EA is found to produce better optimal solutions than a previously reported classical optimization algorithm. Furthermore, the use of a graphical processing unit (GPU) based parallel computing platform demonstrates EA's performance enhancement around 160–700 times in implementing plausibility computations. Handling uncertainties of different types are getting increasingly popular in applied optimization studies and this EA based study is promising to be applied in real-world design optimization problems.

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo .

Improved dwarf mongoose optimization algorithm using novel nonlinear control and exploration strategies

It is well known that in evolutionary algorithms (EAs), different reproduction operators may be suitable for different problems or in different running stages. To improve the algorithm performance, the ensemble of multiple operators has become popular. Most ensemble techniques achieve this goal by choosing an operator according to a probability learned from the previous experience. In contrast to these ensemble techniques, in this paper we propose a cheap surrogate model-based multioperator search strategy for evolutionary optimization. In our approach, a set of candidate offspring solutions are generated by using the multiple offspring reproduction operators, and the best one according to the surrogate model is chosen as the offspring solution. Two major advantages of this approach are: 1) each operator can generate a solution for competition compared to the probability-based approaches and 2) the surrogate model building is relatively cheap compared to that in the surrogate-assisted EAs. The model is used to implement multioperator ensemble in two popular EAs, that is, differential evolution and particle swarm optimization. Thirty benchmark functions and the functions presented in the CEC 2013 are chosen as the test suite to evaluate our approach. Experimental results indicate that the new approach can improve the performance of single operator-based methods in the majority of the functions.

Accurate and efficient prediction of oilfield productivity is very important for the formulation of development and adjustment plans. Machine learning (ML) productivity prediction model can quickly obtain the productivity of oilfield development. In this paper, an oilfield development productivity prediction model based on five ML algorithms including multivariable linear regression (LR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), back propagation (BP) neural network and long short term memory (LSTM) neural network is established. Through the evaluation of model performance indicators (include the root mean square error (RMSE), the coefficient of determination (R2), the mean absolute error (MAE) and mean absolute percentage error (MAPE)), the best performance prediction model is selected. The research results show that the prediction results of LR model are greatly affected by the data of high productivity oil wells, XGBoost model are easily affected by fitting, and BP neural network model is far less effective than other models. Through comprehensive comparison of prediction results, LightGBM model has better stability and generalization performance. The difference between the prediction results of each model is mainly caused by the characteristics of the algorithm and the size of the data sets. At the same time, LSTM can predict the future oil well production based on the oil well time series observation data. The research results of this paper have guiding significance for the selection of productivity prediction model for oilfield development based on data-driven.