Parallel global optimization algorithms in optimal design

Törn, Aimo; Žilinskas, Antanas

doi:10.1007/bfb0008452

Cited by 23 publications

(38 citation statements)

References 5 publications

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“…It was first proposed by Rastrigin [20] as a 2-dimensional function and has been generalized by Mühlenbein [21]. Rastrigin function is an important highly multimodal function which is used in global optimization because it has the property to have large search space and many local minima but only one global minimum.…”

Section: Rastrigin Functionmentioning

confidence: 99%

Particle Swarm Optimization Assisted by Gaussian Processes for Multimodal Function Optimization

Zhang¹,

Zhang²

2016

Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

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Abstract-In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, standard Particle Swarm Optimization (PSO) meets a special challenge for a very large number of problem function evaluations. Applying models as a surrogate of the real fitness function is proved effective way to address this challenge. This study proposes a model assisted PSO, which uses a Gaussian Process (GP) approximation model. In this algorithm, the training datasets for establishing a GP are generated by the first two generations particle information. Once the GP model is obtained, the function value is evaluated using trained GP model instead of real function evaluation, so that the total number of function evaluation is clearly reduced. In order to improve the predictive capacity of GP, the training datasets are dynamically renewed through sorting datasets and replacing the worst dataset during iterative process. Numerical results from simulations on several 20 dimensional complex multimodal functions are presented. Furthermore, a comparison of the new algorithm with the standard PSO is also made. Results show that the new algorithm is much more efficient than standard PSO.

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Section: Rastrigin Functionmentioning

confidence: 99%

Particle Swarm Optimization Assisted by Gaussian Processes for Multimodal Function Optimization

Zhang¹,

Zhang²

2016

Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

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“…On the other side, theorists of Global Optimization claim the absence of real world applications of global algorithms able to effectively test their performances (test functions are generally unable to provide a convincing measure of the attainable performance in practical problems) [21].…”

Section: Introductionmentioning

confidence: 99%

“…The second approach asks for advanced optimization techniques of global nature, able to traps out from local optima and transcend the incumbent solution [20][21][22][23][24][25][26]. Concerning this point, it must be stressed that, as long as the absence of local optima has not been proved, global optimization algorithms should always be used.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, only in the last three decades the need for more accurate solutions to more sophisticated practical problems and the renewal of attention by researchers all over the world have produced a significant theoretical evolution in this area. In particular, new algorithms able to give new solutions to old problems as well as solutions to problems never afforded before have been designed [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, new strategies able to overcome or significantly reduce the occurrence of false solutions are of serious interest [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global Optimization: Applications to Advanced Reflector Antennas Synthesis and Diagnosis Techniques

Capozzoli¹,

D’Elia²

2006

PIER

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Abstract-This is the first of two companion papers on global optimization and antenna analysis and synthesis. In Part I, an analysis of the problems involved in Global Optimization is presented by critically discussing the basic concepts and tools, the performances to be expected, the required computational complexity and the guidelines to select algorithms solving efficiently the problem at hand. The relevance of stochastic techniques is enhanced and the role of double phase algorithms is stressed. The proof of the convergence property of an idealized version of a simplified evolutionary algorithm is provided. In Part II, the selected algorithm, a hybrid evolutionary algorithm, is tested against two real world problems relevant in electromagnetics, the power synthesis of contoured beam hybrid reflector antennas and the reflector antenna diagnosis from only amplitude data. The results of an extensive numerical analysis are presented.

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