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

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.