scholarly journals Polynomial Mean-Centric Crossover for Directed Mating in Evolutionary Constrained Multi-Objective Continuous Optimization

2017 ◽  
Author(s):  
Minami Miyakawa ◽  
Hiroyuki Sato ◽  
Yuji Sato
Keyword(s):  
Continuous Optimization ◽  
Multi Objective

An Intelligent Water Drop Algorithm for Solving Multi-Objective Vehicle Routing Problems With Mixed Time Windows

2019 ◽  
Vol 10 (1) ◽  
pp. 82-104 ◽  
Author(s):  
Tao Wang ◽  
Jing Ni ◽  
Yixuan Wang
Keyword(s):  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Water Drop ◽  
Time Windows ◽  
Continuous Optimization ◽  
Vehicle Routing Problems ◽  
Basic Algorithm ◽  
Routing Problems ◽  
Multi Objective ◽  
Minimum Number

This article proposes an Intelligent Water Drop Algorithm for solving Multi-Objective Vehicle Routing Problems by considering the constraints of vehicle volume, delivery mileage, and mixed time windows and minimizing the cost of distribution and the minimum number of vehicles. This article improves the basic Intelligent Water Drop Algorithm and show the improved intelligent water droplet genetic hybrid algorithm is an effective method for solving discrete problems. The authors present a practical example and show the applicability of the proposed algorithm. The authors compare the algorithms with the basic algorithm and show the improved intelligent droplet genetic hybrid algorithm has higher computing efficiency and continuous optimization capability.

scholarly journals Multi-objective regression modeling for natural gas prediction with ridge regression and CMARS

2022 ◽  
Vol 12 (1) ◽  
pp. 56-65
Author(s):  
Ayse Ozmen
Keyword(s):  
Natural Gas ◽  
Ridge Regression ◽  
Distribution Systems ◽  
Continuous Optimization ◽  
Multivariate Adaptive Regression Splines ◽  
Model Complexity ◽  
Forecast Horizon ◽  
Multi Objective ◽  
Conic Quadratic Programming ◽  
The Impact

Residential customers are the main users generally need a great quantity of natural gas in distribution systems, especially, in the wintry weather season since it is particularly consumed for cooking and space heating. Hence, it ought to be non-interruptible. Since distribution systems have a restricted ability for supply, reasonable planning and prediction through the whole year, especially in winter seasons, have emerged as vital. The Ridge Regression (RR) is formulated mainly to decrease collinearity results through shrinking the regression coefficients and reducing the impact in the model of variables. Conic multivariate adaptive regression splines ((C)MARS) model is constructed as an effective choice for MARS by using inverse problems, statistical learning, and multi-objective optimization theories. In this approach, the model complexity is penalized in the structure of RR and it is constructed a relaxation by utilizing continuous optimization, called Conic Quadratic Programming (CQP). In this study, CMARS and RR are applied to obtain forecasts of residential natural gas demand for local distribution companies (LDCs) that require short-term forecasts, and the model performances are compared by using some criteria. Here, our analysis shows that CMARS models outperform RR models. For one-day-ahead forecasts, CMARS yields a MAPE of about 4.8%, while the same value under RR reaches 8.5%. As the forecast horizon increases, it can be seen that the performance of the methods becomes worse, and for a forecast one week ahead, the MAPE values for CMARS and RR are 9.9% and 18.3%, respectively.

scholarly journals Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3337
Author(s):  
Ruiye Li ◽  
Peng Cheng ◽  
Yingyi Hong ◽  
Hai Lan ◽  
He Yin
Keyword(s):  
Finite Element ◽  
Temperature Distribution ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Element Model ◽  
Optimization Method ◽  
Geometric Parameters ◽  
Maximum Temperature ◽  
Design Decision ◽  
Multi Objective

The extensive use of finite element models accurately simulates the temperature distribution of electrical machines. The simulation model can be quickly modified to reflect changes in design. However, the long runtime of the simulation prevents any direct application of the optimization algorithm. In this paper, research focused on improving efficiency with which expensive analysis (finite element method) is used in generator temperature distribution. A novel surrogate model based optimization method is presented. First, the Taguchi orthogonal array relates a series of stator geometric parameters as input and the temperatures of a generator as output by sampling the design decision space. A number of stator temperature designs were generated and analyzed using 3-D multi-physical field collaborative finite element model. A suitable shallow neural network was then selected and fitted to the available data to obtain a continuous optimization objective function. The accuracy of the function was verified using randomly generated geometric parameters to the extent that they were feasible. Finally, a multi-objective genetic optimization algorithm was applied in the function to reduce the average and maximum temperature of the machine simultaneously. As a result, when the Pareto front was compared with the initial data, these temperatures showed a significant decrease.

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