摘要
1 Introduction Inspired by natural evolution and biological behavior,researchers have developed many successful bio-inspired algorithms.Ant colony optimization(ACO)is one of the most successful bio-inspired computing methods for complex optimization problems.In contrast to the wide range of applications,the theoretical understanding of this kind of algorithms lagged far behind[1].Therefore,it is desirable and necessary to improve the theoretical foundation of the algorithm in order to have a better understanding of the execution mechanism of the algorithm and guide the algorithm design.Many researches are devoted to understanding the working principles of bio-inspired algorithms,and try to bridge the gap between theoretical research and practical applications of the algorithms.Many encouraging results have been obtained[2].
基金
This work was supported by the National Natural Science Foundation of China(Grant Nos.61703183,61773410 and 61876207)
the PublicWelfare Technology Application Research Plan of Zhejiang Province(LGG19F030010)
the Science and Technology Program of Guangzhou(202002030260).
