CAJ | 학술논문

针对原有的多种群蚁群算法收敛速度慢，运行时间长，容易早熟等缺陷，提出了一种新型异类多种群蚁群算法。算法由多类不同特性蚁群构成，不同蚁群具有不同特质，且优势互补，彼此间具有潜在的合作性。不同种类蚁群搜索时，通过子蚁群间的相似度，自适应选择最互补的蚁群进行信息交换，以加强不同种类蚁群间的协作，增强解的多样性，增强跳出局部最优的能力。TSP仿真结果表明，该算法在搜索速度以及搜索质量方面都有明显的提高。
침대원유적다충군의군산법수렴속도만，운행시간장，용역조숙등결함，제출료일충신형이류다충군의군산법。산법유다류불동특성의군구성，불동의군구유불동특질，차우세호보，피차간구유잠재적합작성。불동충류의군수색시，통과자의군간적상사도，자괄응선택최호보적의군진행신식교환，이가강불동충류의군간적협작，증강해적다양성，증강도출국부최우적능력。TSP방진결과표명，해산법재수색속도이급수색질량방면도유명현적제고。
To overcome the problems of searching speed, running time, and earlier premature of traditional multiple ant colonies algorithm, an improved algorithm is proposed. This algorithm introduces more than one type of ant colonies with different pheromone updating mechanisms. These different types of ant colonies have different searching traits. They can cooperate smoothly each other. Every ant colony adaptively chooses suitable information exchangeable object from more than one potential selected ant colonies, determines the best pheromone exchanging strategy from a variety of options through similarity coefficient among every ant colonies. By this way, the balance between the diversity and convergence of every ant colony is kept desirable. A series of TSP experiments show that this algorithm can generate solutions with better quality and faster speed.