The purpose of the paper is to research evolutionary computation, especially Quantum-behaved Particle Swarm Optimization, for solving Multi-objective optimization problem. Multi-objective decision problems exist widely in research and application area, and each objective conflict with each other, it takes the researcher more energy to solve these problems. The research of methods to solve multi-objective optimization problems is of great value in scientific research and practice significance.Evolutionary computation, an effective method of solving complex optimization problem is discussed first. Particle Swarm Optimization, a swarm intelligence algorithm in this field is briefly described. A new global convergence algorithm, Quantum-behaved Particle Swarm Optimization (QPSO for short) algorithm is mainly introduced. Then, the two algorithms, PSO and QPSO are compared with each other, analysis of multi-objective optimization algorithm based on PSO is given.Combining QPSO with other evolution computation techniques to settle Multi-Objective problem, two improved multi-objective particle swarm algorithms based on QPSO are presented, which are called VEQPSO and WAQPSO. VEQPSO is generated by combining assessment method of vector evaluation with QPSO.WAQPSO is by the combination of QPSO and the mind of Weighted Aggregation PSO. We test them on some multi-objective benchmark problems and compare results with VEPSO and WAPSO. These results show: accurate Pareto curves of test functions with QPSO, solution set gained are distributed uniform, and validate the efficiency of QPSO algorithm for solving multi-objective optimization problem.Finally, solving Constrained Optimization problem is regarded as Multi-Objective Optimization problem. Then, WAQPSO algorithm is used to solve it. We test the algorithm, and the results show that WAPQSO is an algorithm with better performance. |