Sphere drag revisited using Shuffled Complex Evolution algorithm

2014 ◽  
pp. 345-353 ◽  
Author(s):  
R Barati ◽  
S Neyshabouri ◽  
G Ahmadi
Keyword(s):  
Shuffled Complex Evolution ◽  
Evolution Algorithm ◽  
Sphere Drag

A Hybrid Pso and Shuffled Complex Evolution Algorithm for Order Assignment Problem in Holonic Manufacturing System

2010 ◽  
Vol 97-101 ◽  
pp. 2213-2216 ◽  
Author(s):  
Fu Qing Zhao ◽  
Jian Hua Zou ◽  
Shang Xiong Sheng
Keyword(s):  
Assignment Problem ◽  
Lead Time ◽  
Manufacturing System ◽  
Evaluation Model ◽  
Pso Algorithm ◽  
Object A ◽  
Shuffled Complex Evolution ◽  
Evolution Algorithm ◽  
Holonic Manufacturing ◽  
Order Assignment

Today’s market dynamics have made operation process in manufacturing system extremely complex and difficult. Enterprise need to continuous adjust its strategies on evaluating and designing their task and order assignment methods to provide products at the lowest possible cost while reducing the total lead time. The evaluation model based on Holonic Manufacturing System is presented in this paper. The model incorporates operation cost and lead time as the object. A hybrid PSO algorithm with Shuffled Complex Evolution Algorithm(SCE) is utilized to compute the combination optimum for Order assignment problem . Simulation result shows that the model and the algorithm are effective to the problem.

scholarly journals Genetic algorithms for real-time operation of multipurpose water resource systems

2004 ◽  
Vol 6 (1) ◽  
pp. 19-38 ◽  
Author(s):  
Alcigeimes B. Celeste ◽  
Koichi Suzuki ◽  
Akihiro Kadota
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real Time ◽  
Water Resource ◽  
Water Resource System ◽  
Shuffled Complex Evolution ◽  
Time Operation ◽  
Real Time Operation ◽  
Evolution Algorithm ◽  
Resource System

This paper deals with the application of genetic algorithms to the operation of a water resource system in real time. A genetic algorithm was developed and applied to solve an optimization model for the operation of the system responsible for the water supply of Matsuyama City, in Japan. For comparison purposes, the same model was solved by a technique based on calculus and the Shuffled Complex Evolution Algorithm. The general characteristics of the algorithms and the results from simulations carried out for various conditions are presented. Genetic algorithms appear to be effective tools for real-time reservoir operation.

scholarly journals Developing a shuffled complex evolution algorithm using a differential evolution algorithm for optimizing hydropower reservoir systems

2017 ◽  
Vol 18 (3) ◽  
pp. 1081-1092 ◽  
Author(s):  
Omid Bakhtiari Nezhad ◽  
Mohsen Najarchi ◽  
Mohammad Mahdi NajafiZadeh ◽  
S. Mohammad Mirhosseini Hezaveh
Keyword(s):  
Water Resources ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Mathematical Function ◽  
Complex Issue ◽  
De Algorithm ◽  
Shuffled Complex Evolution ◽  
Reservoir Systems ◽  
Evolution Algorithm ◽  
Hydropower Reservoir

Abstract The aim of this study is to improve the performance of the shuffled complex evolution (SCE) algorithm used in the optimization of hydropower generation in reservoirs as a complex issue in water resources. First, the SCE algorithm is merged with the differential evolution (DE) algorithm to form the SCE-DE algorithm. Then, a complex mathematical function is used as a benchmark to evaluate the performance and validate the SCE-DE algorithm and the outcomes are compared with the original SCE algorithm to show the superiority of the proposed SCE-DE algorithm. In addition, the two-reservoir system of Dez-Gotvand is considered as a real optimization problem to evaluate the performance of the SCE-DE algorithm. It is revealed that optimization by SCE-DE is much better than SCE. In conclusion, the results show that the proposed SCE-DE algorithm is a reasonable alternative to optimizing resource systems and can be used to solve complex issues of water resources.

scholarly journals Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition-based learning

2019 ◽  
Vol 158 ◽  
pp. 991-997 ◽  
Author(s):  
Yixiang Chen ◽  
Zhicong Chen ◽  
Lijun Wu ◽  
Chao Long ◽  
Peijie Lin ◽  
...  
Keyword(s):  
Parameter Extraction ◽  
Opposition Based Learning ◽  
Shuffled Complex Evolution ◽  
Evolution Algorithm
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