DocumentCode :
2727283
Title :
Intelligent particle swarm optimization in multiobjective optimization
Author :
Xiao-Hua, Zhang ; Hong-Yun, Meng ; Li-cheng, Jiao
Author_Institution :
Inst. of Intelligent Inf. Process., Xidian Univ., Xi´´an
Volume :
1
fYear :
2005
fDate :
5-5 Sept. 2005
Firstpage :
714
Abstract :
How to find a sufficient number of uniformly distributed and representative Pareto optimal solutions is very important for multiobjective optimization (MO) problems. A new model for particle swarm optimization is constructed firstly, and then an intelligent particle swarm optimization (IPSO) for MO problems is proposed based on AER (agent-environment-rules) model, in which competition operator and clonal selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. The quantitative and qualitative comparisons indicate that the proposed approach is highly competitive and that can be considered as a viable alternative to solve MO problems
Keywords :
Pareto optimisation; multi-agent systems; particle swarm optimisation; Pareto optimal solution; agent-environment-rules model; clonal selection operator; competition operator; intelligent particle swarm optimization; multiobjective optimization; Birds; Filters; Heuristic algorithms; Intelligent agent; Pareto optimization; Particle swarm optimization; Proposals; Propulsion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Conference_Location :
Edinburgh, Scotland
Print_ISBN :
0-7803-9363-5
Type :
conf
DOI :
10.1109/CEC.2005.1554753
Filename :
1554753
Link To Document :
بازگشت