Introduction to Extremal Optimization

2018 ◽  
pp. 21-35
Author(s):  
Yong-Zai Lu ◽  
Yu-Wang Chen ◽  
Min-Rong Chen ◽  
Peng Chen ◽  
Guo-Qiang Chen
Keyword(s):  
Extremal Optimization

Multiobjective extremal optimization with applications to engineering design

2007 ◽  
Vol 8 (12) ◽  
pp. 1905-1911 ◽  
Author(s):  
Min-rong Chen ◽  
Yong-zai Lu ◽  
Gen-ke Yang
Keyword(s):  
Engineering Design ◽  
Extremal Optimization

scholarly journals Optimal P-Q Control of Grid-Connected Inverters in a Microgrid Based on Adaptive Population Extremal Optimization

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2107 ◽  
Author(s):  
Min-Rong Chen ◽  
Huan Wang ◽  
Guo-Qiang Zeng ◽  
Yu-Xing Dai ◽  
Da-Qiang Bi
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Active Power ◽  
Adaptive Genetic Algorithm ◽  
Constrained Optimization Problem ◽  
Extremal Optimization ◽  
Active And Reactive Power ◽  
Three Phase ◽  
Grid Connected Inverter

The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.

GEO + ES Hybrid Optimization Algorithm Applied to the Parametric Thermal Model Estimation of a 200N Hydrazine Thruster

2011 ◽  
Author(s):  
Roberto Luiz Galski ◽  
Heitor Patire Ju´nior ◽  
Fabiano Luis de Sousa ◽  
Jose´ Nivaldo Hinckel ◽  
Pedro Lacava ◽  
...  
Keyword(s):  
Temperature Profile ◽  
Film Cooling ◽  
Simulated Data ◽  
Convergence Properties ◽  
Extremal Optimization ◽  
Hybrid Optimization Algorithm ◽  
Design Variables ◽  
Desirable Feature ◽  
Self Tuning ◽  
Generalized Extremal Optimization

In the present paper, a hybrid version of the Generalized Extremal Optimization (GEO) and Evolution Strategies (ES) algorithms [1], developed in order to conjugate the convergence properties of GEO with the self-tuning characteristics present in the ES, is applied to the estimation of the temperature distribution of the film cooling near the internal wall of a thruster. The temperature profile is determined through an inverse problem approach using the hybrid. The profile was obtained for steady-state conditions, were the external wall temperature along the thruster is considered as a known input. The Boltzmann’s equation parameters [2], which define the cooling film temperature profile, are the design variables. Results using simulated data showed that this approach was efficient in recuperating those parameters. The approach showed here can be used on the design of thrusters with lower wall temperatures, which is a desirable feature of such devices.

Generalized External Optimization

2005 ◽  
pp. 41-60 ◽  
Author(s):  
Fabiano Luis de Sousa ◽  
Fernando Manuel Ramos ◽  
Roberto Luiz Galski ◽  
Issamu Muraoka
Keyword(s):  
Optimization Problems ◽  
Evolutionary Model ◽  
Design Tool ◽  
Thermal Design ◽  
Extremal Optimization ◽  
Integer Variables ◽  
Constrained Problems ◽  
Self Organized ◽  
Complex Design ◽  
Self Organized Criticality

In this chapter a recently proposed meta-heuristic devised to be used in complex optimization problems is presented. Called Generalized Extremal Optimization (GEO), it was inspired by a simple co-evolutionary model, developed to show the emergence of self-organized criticality in ecosystems. The algorithm is of easy implementation, does not make use of derivatives and can be applied to unconstrained or constrained problems, non-convex or even disjoint design spaces, with any combination of continuous, discrete or integer variables. It is a global search meta-heuristic, like the Genetic Algorithm (GA) and the Simulated Annealing (SA), but with the advantage of having only one free parameter to adjust. The GEO has been shown to be competitive to the GA and the SA in tackling complex design spaces and a useful tool in real design problems. Here the algorithm is described, including a step-by-step implementation to a simple numerical example, its main characteristics highlighted, and its efficacy as a design tool illustrated with an application to satellite thermal design.

Multi-Objective Extremal Optimization in Processor Load Balancing for Distributed Programs

2018 ◽  
pp. 176-188
Author(s):  
Ivanoe De Falco ◽  
Eryk Laskowski ◽  
Richard Olejnik ◽  
Umberto Scafuri ◽  
Ernesto Tarantino ◽  
...  
Keyword(s):  
Load Balancing ◽  
Distributed Programs ◽  
Extremal Optimization ◽  
Multi Objective ◽  
Processor Load
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