Title :
PSO versus GAs for fast object localization problem
Author :
Xinjian Fan ; Xuelin Wang ; Yongfei Xiao
Author_Institution :
Shandong Provincial Key Lab. of Robot & Manuf. Autom. Technol. (SPKLRMAT), Inst. of Autom., Jinan, China
Abstract :
Particle swarm optimization (PSO) and genetic algorithms (GAs) are two kinds of widely used evolutionary compution techniques. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for the object localization problem. The problem of object localization can be formulated into an integer nonlinear optimization problem (INOP). We respectively expand the basic PSO and GA to solve the formulated INOP. Experiments were made on a set of 42 test images with complex backgrounds. The results show that although GA and PSO share many common features, PSO is more suitable for the problem than GA.
Keywords :
genetic algorithms; object detection; particle swarm optimisation; GA; PSO; evolutionary compution technique; genetic algorithm; integer nonlinear optimization problem; object localization problem; particle swarm optimization; Face; Genetic algorithms; Genetics; Optimization; Particle swarm optimization; Sociology; Statistics;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463237