针对约束优化问题的求解，提出了一种结合ε约束处理法和纵横交叉策略的野狗优化算法（εCDOA）。算法先在野狗优化算法的所有个体选择捕猎策略更新位置后引入纵横交叉策略，以提高所得算法的全局搜索和局部搜索能力，也有利于算法跳出局部最优；然后按照ε约束处理法，将等式约束转化为不等式约束，并用ε水平比较方法代替适应度值比较来评判算法中野狗个体的优劣；最后根据个体的约束违约度按自适应的ε值把种群分成两个子群，分别以子群的存活策略计算个体的存活率并更新存活率低的个体。对CEC 2006中19个标准约束优化问题进行数值实验，数值实验结果显示，算法εCDOA比结合ε约束处理法的野狗优化算法等4种对比算法有更好的寻优效果。对于3种经典工程设计问题，算法εCDOA给出的设计方案明显优于其他对比算法给出的方案。

In order to solve constrained optimization problems, a dingo optimization algorithm with ε constrained method and crisscross strategy (εCDOA) was proposed. The algorithm first introduces the crisscross strategy in dingo optimization algorithm after all individuals select one hunting strategy and update their positions, so as to improve the global and local search capabilities of the obtained algorithm, which also help the algorithm jump out of the local optimum. Then, according to ε constrained method, the equality constraints were transformed into the inequality constraints, and the ε level comparison method was used instead of fitness value comparison to evaluate the qualities of the dingoes. Finally, based on the individuals' constraint violations, the population is divided into two subgroups according to adaptive ε values. The individuals' survival rates were calculated using the survival strategy of each subgroup, and the individuals with low survival rates were updated. The results of numerical experiments on 19 standard constrained optimization problems in CEC 2006 show that algorithm εCDOA has better optimization performance than four comparative algorithms such as dingo optimization algorithm with ε constrained method. For three classical engineering design problems, the design schemes given by algorithm εCDOA are obviously better than those given by other algorithms.