A new idea for addressing multi-objective combinatorial optimization: Quantum multi-agent evolutionary algorithms

Author

Zhao, Dongming ; Tao, Fei

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Michigan, Dearborn, MI

fYear

2009

fDate

18-20 March 2009

Firstpage

28

Lastpage

31

Abstract

Multi-objective combinatorial optimization (MOCO) problem is investigated in this paper. Combining the characters of agent and quantum-bit, a new idea, i.e., Quantum multi-agent evolutionary algorithms (QMAEA), for addressing MOCO problem is proposed. In QMAEA, each agent represented with quantum-bit is defined as a solution. Several operations such as evaluation-operation, competition-operation, mutation-operation, and local-evolution-Operation are introduced in QMAEA. The working flow of QMAEA is presented.

Keywords

combinatorial mathematics; evolutionary computation; competition-operation; evaluation-operation; local-evolution-operation; multi-objective combinatorial optimization; mutation-operation; quantum multi-agent evolutionary algorithms; quantum-bit; Computer aided manufacturing; Evolutionary computation; Laboratories; Quantum computing; State-space methods; USA Councils; Quantum; Quantum multi-agent evolutionary algorithms; agent; multi-objective combinatorial optimization (MOCO);

fLanguage

English

Publisher

ieee

Conference_Titel

Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on

Conference_Location

Baltimore, MD

Print_ISBN

978-1-4244-2733-8

Electronic_ISBN

978-1-4244-2734-5

Type

conf

DOI

10.1109/CISS.2009.5054684

Filename

5054684