DocumentCode :
3457504
Title :
An Extended Artificial Physics Optimization Algorithm for Global Optimization Problems
Author :
Xie, Liping ; Zeng, Jianchao
Author_Institution :
Coll. of Electr. & Inf. Eng., Lanzhou Univ. of Technol., Lanzhou, China
fYear :
2009
fDate :
7-9 Dec. 2009
Firstpage :
881
Lastpage :
884
Abstract :
Artificial Physics Optimization (APO) algorithm inspired by natural physical forces is a population-based stochastic algorithm based on Physicomimetics framework. In this paper, an extended APO (EAPO) algorithm is presented through considering the personal best positions of all individual, which can provide much useful information for search. In EAPO algorithm, the velocity updated equation is similar to that of PSO algorithm. By comparison and analysis, we can consider that EAPO algorithm is a general form of PSO algorithm and has a better diversity than PSO algorithm. The simulation results confirm that EAPO is an effective stochastic population-based search algorithm. Meanwhile, a comparison with other population-based heuristics shows that EAPO algorithm is competitive.
Keywords :
particle swarm optimisation; search problems; stochastic processes; PSO algorithm; Physicomimetics; artificial physics optimization algorithm; global optimization; stochastic population-based search algorithm; Animals; Computational intelligence; Educational institutions; Electronic mail; Laboratories; Multirobot systems; Particle swarm optimization; Physics computing; Robots; Stochastic systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4244-5543-0
Type :
conf
DOI :
10.1109/ICICIC.2009.86
Filename :
5412398
Link To Document :
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