A Multiagent Quantum Evolutionary Algorithm for Global Numerical Optimization

In this paper, a novel kind of algorithm, multiagent quantum evolutionary algorithm (MAQEA), is proposed based on multiagent, evolutionary programming and quantum computation. An agent represents a candidate solution for optimization problem. All agents are presented by quantum chromosome, whose core lies on the concept and principles of quantum computing, live in table environment. Each agent competes and cooperates with its neighbors in order to increase its competitive ability. Quantum computation mechanics is employed to accelerate evolution process. The result of experiments shows that MAQEA has a strong ability of global optimization and high convergence speed.

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