摘要
探讨求解双目标区间值规划的免疫遗传算法。算法设计中,利用个体间的支配关系,将种群划分为优质、劣质种群,并沿着不同进化方式产生优质和多样个体;利用新拥挤模型,剔除种群中冗余个体,确保进化种群中个体分布的均匀性。数值比较实验表明,该算法在获解质量和解分布方面有一定优势。
tigated. In lmions by For the problem of bi-objective interval-valued programming, an immune genetic algorithm was inves- the design of algorithm, the current population is divided into superior and inferior-quality sub-popu- taking advantage of the relationship of dominance between individuals. Such two subpopulations create excellent and diverse individuals through evolution along different evolutionary directions. In the process of solu- tion search, some redundant individuals are eliminated by means of a new crowding model, which ensures that those individuals in the population have uniform distributions. Numerically comparative results have showed that the algorithm is of potential for bi-objective interval-valued programming problems, with respect to solution quali- ty and solution distribution.
出处
《贵州大学学报（自然科学版）》
2016年第1期82-85,共4页
Journal of Guizhou University:Natural Sciences
基金
国家自然科学基金项目资助(61563009)
教育部博士点基金项目资助(20125201110003)
贵州大学研究生创新基金项目资助(研理工2015057)
关键词
双目标区间值规划
免疫遗传算法
区间分析
拥挤度
bi-objective interval-valued programming
immune genetic algorithm
interval analysis
crowding degree
