Generalized Differential Evolution for General Non-Linear Optimization

COMPSTAT 2008 ◽  
2008 ◽  
pp. 459-471 ◽  
Author(s):  
Saku Kukkonen ◽  
Jouni Lampinen
Keyword(s):  
Differential Evolution ◽  
Linear Optimization ◽  
Non Linear ◽  
Generalized Differential

scholarly journals Adaptive Unified Differential Evolution Algorithm for Optimal Operation of Power Systems with Static Security, Transient Stability and SSSC Device

2019 ◽  
Vol 9 (1) ◽  
pp. 2238-2353
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Transient Stability ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Optimal Operation ◽  
Facts Devices ◽  
Non Linear ◽  
Linear Optimization Problems

The environmental degradation and increased power demand has forced modern power systems to operate at the closest stability boundaries. Thereby, the power systems operations mainly focus for the inclusion of transient stability constraints in an optimal power flow (OPF) problem. Algebraic and differential equations are including in non-linear optimization problems formed by the transient stability constrained based OPF problem (TSCOPF). Notably, for a small to large power systems solving these non-linear optimization problems is a complex task. In order to achieve the increased power carrying capacity by a power line, the Flexible AC transmission systems (FACTS) devices provides the best supported means a lot. As a result, even under a network contingency condition, the security of the power system is also highly improved with FACTS devices. The FACTS technology has the potential in controlling the routing of the line power flows and the capability of interconnecting networks making the possibility of trading energy between distant agents. This paper presents a new evolutionary algorithm for solving TSCOPF problems with a FACTS device namely adaptive unified differential evolution (AuDE). The large non-convex and nonlinear problems are solved for achieving global optimal solutions using a new evolutionary algorithm called AuDE. Numerical tests on the IEEE 30-bus 6-generator, and IEEE New England 10-generator, 39-bus system have shown the robustness and effectiveness of the proposed AuDE approach for solving TSCOPF in the presence of a FACTS device such as the SSSC device. Due to the page limitation only 30-bus results are presented.

GDE4: The Generalized Differential Evolution with Ordered Mutation

2019 ◽  
pp. 101-113 ◽  
Author(s):  
Azam Asilian Bidgoli ◽  
Sedigheh Mahdavi ◽  
Shahryar Rahnamayan ◽  
Hessein Ebrahimpour-Komleh
Keyword(s):  
Differential Evolution ◽  
Generalized Differential

Non-rigid structure from motion using ranklet-based tracking and non-linear optimization

2007 ◽  
Vol 25 (3) ◽  
pp. 297-310 ◽  
Author(s):  
A. Del Bue ◽  
F. Smeraldi ◽  
L. Agapito
Keyword(s):  
Structure From Motion ◽  
Linear Optimization ◽  
Rigid Structure ◽  
Non Linear

scholarly journals Design of a New Synthetic Acceptance Sampling Plan

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 653 ◽  
Author(s):  
Saeed Dobbah ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Operating Characteristic ◽  
Linear Optimization ◽  
Sampling Plan ◽  
Quality Characteristic ◽  
Acceptance Sampling ◽  
Acceptance Sampling Plan ◽  
Non Linear

In this paper, we propose a new synthetic sampling plan assuming that the quality characteristic follows the normal distribution with known and unknown standard deviation. The proposed plan is given and the operating characteristic (OC) function is derived to measure the performance of the proposed sampling plan for some fixed parameters. The parameters of the proposed sampling plan are determined using non-linear optimization solution. A real example is added to explain the use of the proposed plan by industry.

Analytical temperature predictive modeling and non-linear optimization in machining

2019 ◽  
Vol 102 (5-8) ◽  
pp. 1557-1566 ◽  
Author(s):  
Elham Mirkoohi ◽  
Peter Bocchini ◽  
Steven Y. Liang
Keyword(s):  
Predictive Modeling ◽  
Linear Optimization ◽  
Non Linear

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

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