摘要
依据免疫机理及遗传算法,设计免疫遗传算法解决项目计划管理中资源受限、工期最短问题。算法设计中,记忆池用于搜集算法进化获得的优秀个体,并使记忆池中的个体参与交叉;基于群体多样性特征,设计浓度方案调节进化群体的多样性,并用于群体更新;利用自适应变异及修补思想增强进化群体的散布性和修正非可行解。数值实验及比较表明,该算法具有很好的搜索性能,在搜索效果上较为稳定。
Based on immune metaphors and the basic genetic algorithm, an immune-genetic algorithm is proposed to solve the resource-constrained project scheduling problem. In design of the algorithm, memory pool is adopted to collect excellent individuals from the current evolving population, while individuals from the pool must participate in crossover; on the other hand, a density scheme, relying on population＇ s diversity, is designed to update evolving populations and adjust their diversity. Besides, distribution of evolving populations can be strengthened through adaptive mutation, while infeasible solutions are transformed into feasible ones through utilizing the idea of the repairing method. The Experimental results and comparison illustrate the algorithm is of highly superior searching performance and stable performance effect.
出处
《贵州大学学报（自然科学版）》
2007年第3期268-273,共6页
Journal of Guizhou University:Natural Sciences
关键词
免疫算法
遗传算法
资源受限—工期最短
组合优化
Immune algorithm
genetic algorithms
resource-constrained project scheduling
combinatorial optimization
