Multi-agent Genetic Algorithm Based on Self-Adaptive Operator

Author

Shi, LianShuan ; Wang, HuaHui

Author_Institution

Sch. of Inf. Technol. Eng., Tianjin Univ. of Technol. & Educ., Tianjin, China

fYear

2012

fDate

1-3 Nov. 2012

Firstpage

105

Lastpage

108

Abstract

The crossover and mutation rates are two important parameters of multi-objective evolutionary algorithm. The agent technology is applied to solve multi-objective problem. A new multi-objective genetic algorithm based on self-adaptive agent (SAMOGA) is proposed, in which the evolution parameters is adjusted adaptively in the evolutionary process and a new selection operator is used to select individual. The algorithm is applied to several multi-objective test functions, the simulation results show that the algorithm can converge to the Pareto solutions quickly, and has a well diversity compared with NSGA-II.

Keywords

Pareto optimisation; convergence; genetic algorithms; mathematical operators; multi-agent systems; NSGA-II; Pareto solution; SAMOGA; agent technology; convergence; crossover rate; evolution parameter; evolutionary process; multiagent genetic algorithm; multiobjective evolutionary algorithm; multiobjective genetic algorithm; multiobjective problem; multiobjective test function; mutation rate; selection operator; self-adaptive agent; self-adaptive operator; Evolutionary computation; Genetic algorithms; Pareto optimization; Sociology; Vectors; genetic algorithm; multi-agent; multi-objective optimization; self-adaptive operator;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Networks and Intelligent Systems (ICINIS), 2012 Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4673-3083-1

Type

conf

DOI

10.1109/ICINIS.2012.25

Filename

6376496