A Big Data based Approach to Chance Constrained Problems Using Weighted Stratified Sampling and Differential Evolution

2019 ◽  
Author(s):  
Kiyoharu Tagawa
Keyword(s):  
Big Data ◽  
Differential Evolution ◽  
Stratified Sampling ◽  
Constrained Problems

scholarly journals An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 32
Author(s):  
Kiyoharu Tagawa
Keyword(s):  
Differential Evolution ◽  
Information Technologies ◽  
Flood Control ◽  
Stratified Sampling ◽  
Data Sets ◽  
Data Set ◽  
Relaxation Problem ◽  
Constrained Problems ◽  
Huge Data ◽  
Adaptive Differential Evolution

In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP.

scholarly journals The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1909
Author(s):  
Petr Bujok
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Real Life ◽  
Efficient Solutions ◽  
Proper Position ◽  
The Real ◽  
Constrained Problems ◽  
De Algorithm ◽  
Real World Application ◽  
Euclidean Distances

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.

Stratified Sampling Differential Evolution Algorithm for Global Optimization Problem

2012 ◽  
Vol 452-453 ◽  
pp. 1491-1495
Author(s):  
Shu Hua Wen ◽  
Qing Bo Lu ◽  
Xue Liang Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Sampling Method ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Stratified Sampling ◽  
Local Optima ◽  
De Algorithm ◽  
Local Optimal Solution ◽  
Evolution Algorithm

Differential Evolution (DE) is one kind of evolution algorithm, which based on difference of individuals. DE has exhibited good performance on optimization problem. However, when a local optimal solution is reached with classical Differential Evolution, all individuals in the population gather around it, and escaping from these local optima becomes difficult. To avoid premature convergence of DE, we present in this paper a novel variant of DE algorithm, called SSDE, which uses the stratified sampling method to replace the random sampling method. The proposed SSDE algorithm is compared with some variant DE. The numerical results show that our approach is robust, competitive and fast.

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