摘要： 通过对鳗鱼生活行为的分析与研究，提出一种离散问题的新型鳗鱼群智能算法。描述鳗鱼洄游中的行为，提取鳗鱼浓度适应、邻近学习、性别突变3个重要行为，并建立模型进行数学描述。通过对鳗鱼3个重要行为的合理组织，引入等级划分制度与标识度的思想，给出应用于组合优化问题的离散型鳗鱼算法，特别是对于离散个体间的邻近学习，采用切割片段法，使种群个体间的信息可以相互传递。通过TSP问题公共测试库TSPLIB中的数据对算法进行测试，结果表明，该算法具有较强的寻优能力。
关键词:
等级划分,
割片段法,
SP问题,
化问题,
散算法,
智能算法
Abstract: Through analyzing and extracting the eel life behavior, this paper presents a new eels swarm intelligence algorithm for discrete problem. It describes the behavior of migratory eels, extracting three important behaviors which are concentration adaption, neighboring learning and sex mutation, and establishing a model for the mathematical description of the three important behaviors. Based on rational organization of the three important behaviors of eel and introduced of classification system and the thought of identification degrees, the discrete eel algorithm is proposed to combinatorial optimization problems. Especially for neighboring learning among discrete individuals, the paper uses a new method of cutting fragments, so that the information can be passed between individuals of the population to each other. Through the TSPLIB public test library of TSP problem to test the algorithm, results show that the algorithm has strong optimization capability.
Key words:
degree division,
cutting fragment method,
TSP problem,
optimization problems,
discrete algorithm,
swarm intelligence algorithm
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