Parallel Genetic Algorithm with Social Interaction for solving Constrained Global Optimization Problems

被引：0

作者：

Pereira, Rodrigo Lisboa ^{[1
]}

Mollinetti, Marco A. Florenzano ^{[2
]}

Yasojima, Edson Koiti ^{[1
]}

Teixeira, Otavio Noura ^{[3
]}

de Oliveira, Rodrigo M. S. ^{[4
]}

Limao de Oliveira, Roberto C. ^{[5
]}

机构：

[1] Fed Univ Para UFPA, Elect Engn Post Grad Program, Belem, Para, Brazil

[2] Univ Tsukuba, Syst Optimizat Lab, Tsukuba, Ibaraki, Japan

[3] Fed Univ Para UFPA, Comp Engn Post Grad Program, Tucurui, Para, Brazil

[4] Fed Univ Para UFPA, Lab Electromagnet LeMag, Belem, Para, Brazil

[5] Fed Univ Para UFPA, Lab Bionspired Comp LCBIO, Belem, Para, Brazil

来源：

PROCEEDINGS OF THE 2015 SEVENTH INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2015) | 2015年

关键词：

Genetic Algorithm; Social Interaction; Game Theory; Parallel computing; Function Optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

the following paper introduces a parallel approach to a social variant of the Genetic Algorithm, called Parallel Genetic Algorithm with Social Interaction (PSIGA). The algorithm is based on social games involving game theory, and it is implemented using the OpenMP API, which is based on the shared memory programming model for multiple processor architectures. The main contribution of this approach is the parallelization using the Shared Memory of the Social Interaction Genetic Algorithm (SIGA) in order to achieve faster and better optimality than its nonparallel counterpart for global optimization problems with restrictions. For means of performance assessment, the algorithm is tested on four instances of engineering design problems and the obtained results compared with the Genetic Algorithm with Social Interaction (SIGA) implemented in sequential programming model.

引用

页码：351 / 356

页数：6

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