Aiming at the slow convergence speed by using the cold start only, the poor adaptiveness of a single crossover operator and the poor diversity of the normal mutation, a mixture crossover dynamic constrained multi-objective evolutionary algorithm based on self-adaptive start-up strategy is proposed. Firstly, the hybrid cold-and-hot start-up mode is designed to identify the change degree of dynamic environment and the Cauchy mutation is used to enhance the diversity of evolutionary population. Then, to enhance the adaptiveness of crossover operation to the dynamic environment, three classical crossover operators, BLXα,SBX and DE, are used simultaneously, and the respective competitiveness are adjusted adaptively according to their contributions. Finally, the cooperation of the elitist population and the evolutionary population balance the global searching ability and the local searching ability. The simulation results on 6 standard testing functions show that the proposed algorithm not only can dynamically identify the change degree in different environments and improve dynamic tracking effect by enhancing the diversity of initial population, but also can choose crossover operators automatically to accelerate the convergence.%针对单独采用冷启动方式而出现再次收敛速度慢、单种交叉算子自适应不足以及正态变异多样性程度偏弱等问题，提出一种基于自适应启动策略的新型混合交叉动态约束多目标优化算法。在算法设计中，首先采用冷热混合方式识别环境动态调整的程度，并引用柯西变异增强多样性；然后混合BLX α、SBX和DE三种差分进化经典交叉算子，并通过各自贡献度自适应调整其竞争力，以增强交叉操作对环境动态变化的自适应性；最后采用精英与进化两个群体相互协作，进一步均衡算法的局部和全局搜索能力。在6个标准测试函数上的仿真结果表明，该算法能在不同环境下动态识别调整的程度，增加初始种群多样性以提高算法的跟踪效果，且能在同一环境下自适应调整交叉算子以提高算法的收敛速度。
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