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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。
By analyzing and studying the life behavior of eel, a new intelligent algorithm of eel swarm based on discrete problem is proposed. Describe the behavior of migratory eels, extraction of eel concentration adaptation, proximity learning, gender mutation three important behaviors, and the establishment of a mathematical description of the model. By reasonably organizing the three important behaviors of eel and introducing the idea of hierarchy and labeling degree, a discrete eel algorithm which is applied to combinatorial optimization problems is given. Especially for the adjacent learning between discrete individuals, the cutting segment method is used Information between individuals in the population can be passed on to each other. The algorithm is tested by TSPLIB data in TSP public test library. The results show that the algorithm has strong ability of searching for optimal solutions.