Title :
Multi-objective Comprehensive Learning Particle Swarm Optimization based on summation of normalized objectives and diversified selection
Author :
Bo Zheng ; Qu, B.Y. ; Liang, J.J. ; Hui Song
Author_Institution :
Sch. of Electr. Eng., Zhengzhou Univ., Zhengzhou, China
fDate :
May 31 2014-June 2 2014
Abstract :
In this paper, a fast-sorting method called summation of normalized objectives and diversified selection (SNOV-DS) is embedded in Comprehensive Learning Particle Swarm Optimization (CLPSO) to solve multi-objective problems. Due to this method, the simulation time will be decreased. The convergence to true Pareto front and the spread of solutions can also be improved. The algorithm is tested on a set of commonly used multi-objective benchmark functions. The simulation results show that the proposed algorithm is competitive in terms of both performance and running speed.
Keywords :
Pareto optimisation; learning (artificial intelligence); sorting; CLPSO; Pareto front; SNOV-DS; diversified selection; fast-sorting method; multiobjective benchmark functions; multiobjective comprehensive learning particle swarm optimization; multiobjective problems; simulation time; summation; Educational institutions; Measurement; Optimization; Particle swarm optimization; Reactive power; Sociology; Statistics; Comprehensive Learning Particle Swarm Optimization; Evolutionary Algorithms; Multi-objective optimization; non-domination sorting;
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
DOI :
10.1109/CCDC.2014.6852374
