Study of population diversity of multiobjective evolutionary algorithm based on immune and entropy principles

Author

Cui, Xunxue ; Li, Miao ; Fang, Tingjian

Author_Institution

Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China

Volume

2

fYear

2001

fDate

2001

Firstpage

1316

Abstract

A key problem in a multiobjective evolutionary system is how to take measures to preserve diversity in the population. The mechanism of natural immune system and entropy principle are applied in a multiobjective evolutionary process to solve this problem and a strategy of preserving diversity in the population of a multiobjective evolutionary algorithm based on immune and entropy principles is introduced. The detailed design method is shown. Finally, we describe the computer simulation of implementing several two-objective flow shop scheduling problems and compare the computing results of the new method with the multiobjective genetic algorithm. Experimental results show that this strategy can effectively preserve population diversity and it has good search performance

Keywords

evolutionary computation; production control; scheduling; search problems; computer simulation; entropy principle; multiobjective evolutionary algorithm; multiobjective genetic algorithm; natural immune system; population diversity; search performance; two-objective flow shop scheduling; Automation; Boosting; Computer simulation; Design methodology; Entropy; Evolutionary computation; Genetic algorithms; Immune system; Processor scheduling; Space exploration;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934343

Filename

934343