Particle swarm with extended memory for multiobjective optimization

Author

Hu, Xiaohui ; Eberhart, Russell C. ; Shi, Yuhui

Author_Institution

Dept. of Biomed. Eng., Purdue Univ., West Lafayette, IN, USA

fYear

2003

fDate

24-26 April 2003

Firstpage

193

Lastpage

197

Abstract

This paper presents a modified dynamic neighborhood particle swarm optimization (DNPSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. An extended memory is introduced to store global Pareto optimal solutions to reduce computation time. Several benchmark cases were tested and the results show that the modified DNPSO is much more efficient than the original DNPSO and other multiobjective optimization techniques.

Keywords

Pareto optimisation; evolutionary computation; search problems; DNPSO algorithm; computation time; dynamic neighborhood particle swarm optimization; extended memory; global Pareto optimal solutions; multiobjective optimization problems; one-dimension optimization; particle memory updating; Benchmark testing; Biomedical engineering; Equations; Evolutionary computation; Optimization methods; Particle swarm optimization; Random number generation; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Swarm Intelligence Symposium, 2003. SIS '03. Proceedings of the 2003 IEEE

Print_ISBN

0-7803-7914-4

Type

conf

DOI

10.1109/SIS.2003.1202267

Filename

1202267