Abstract: The standard ant system is suitable for the discrete optimization problem, but it lacks special mechanism for dealing with constraints.In this paper, a constrained ant colony system(CACS) for solving constraint-handling optimization problem was proposed, which was based on the biology behavior of ant colony foraging and the heuristic rules for estimating the quality of food source.The mechanism of group recruitment and mass recruitment, which were used to guide the ant colony to search the best solution in the feasible region, were embedded to the ant colony system.To illustrate the effectiveness of proposed algorithm, two benchmark functions were used, the results demonstrated better performance of CACS for achieving global optimal.Furthermore, CACS was applied to the process optimization of butene alkylation.The satisfactory result demonstrated the effectiveness of CACS.

摘要： 经典蚁群系统只适用于离散问题，缺少处理约束的专门机制.基于蚁群觅食的生物学行为，以搜索最优食物源为目标，将约束纳入食物源优劣评价的启发式规则，采用成群募集和海量募集两种方式，并辅以局部搜索，以此引导蚁群寻找可行域中的最优解，构建为适用于连续约束优化问题的蚁群系统（constrained ant colony system, CACS）.测试实例表明，CACS具有良好的适用性及全局优化性能，将它应用于丁烯烷化过程的约束优化，取得了令人满意的结果.