A multi-agent quantum evolutionary algorithm for multi-objective problem and it´s application on PID parameter tuning

The target to solve multiobjective optimization problems (MOOP) is to find as many Pareto-optimal solutions as possible. A new algorithm aimed to solve MOOP was proposed in this paper - multi-agent quantum evolutionary algorithm (MAQEA) on the basis of quantum mechanics theory, the study and competition ability of multi-agent system and organic evolutionary strategy. In the multi-agent system, the agents learn from and compete with others in neighborhood under the quantum evolution mechanism. From the minimization results of two duality functions, the proposed algorithm can find evenly distributed Pareto-optimal solutions effectively. Furthermore, this algorithm was applied to the PID controller parameter tuning. The simulation result showed that this algorithm can obtain different optimal controller parameters with respective targets.