基于抗体的蚁群优化算法研究

针对蚁群优化（ant colony optimization,ACO）容易陷入局部最优，提出一个基于抗体的新型蚁群优化算法(ant colony optimization based on immune algorithm,ACOI)。ACOI是利用免疫算法中抗体的概念来改善人工蚂蚁搜寻解空间的方式，使人工蚂蚁不仅会依随费洛蒙的指引，还会受到抗体的影响去搜寻解空间；而抗体也会随着环境的改变，使抗体成为有效的及无效的2种情形，有效的抗体对人工蚂蚁会有影响，无效的抗体则没有影响。用旅行销售员问题（traveling salesmen problem,TSP）验证ACOI的效能，并与ACO做比较，证明了在蚁群系统中加入抗体要比单纯的蚁群系统效率更高。

Research on ant colony optimization algorithm based on antibody

The paper proposed a new type of antibody based ant colony optimization (ACO), known as the ACOI(ACO based on immune algorithm). ACOI was the use of antibodies in the concept of immune algorithm to improve the ways of artificial ants search solution space in which artificial ants would not only follow the guidelines with the pheromone, but also search the solution space by the impact of antibodies. The antibody would be effective or ineffective as environments change. The effective antibody had an impact on artificial ants, while ineffective not. ACOI proved by traveling salesman problem doing validation, and compared with the ACO, shows that adding antibodies to ants group is more effecibe than a simple ant colony system.