Title :
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
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);
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
DOI :
10.1109/CISS.2009.5054684
