改进遗传算法在MSPSP问题中的验证
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中央高校基本科研业务费专项(2242020K40244)


Verification of Improved Genetic Algorithm in MSPSP Problem
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    摘要:

    为了求解多技能资源受限项目调度问题(MSPSP),本文提出了一种改进遗传算法.首先根据问题的数学模型,确立了基于优先权的实数编码方式,并将目标函数转为适应度函数以供后续适应度的计算;接着将基于群体共享的小生境技术融入到遗传算法的选择过程中,并借助确定式采样选择和子种群的调整进一步提高算法的搜索能力;然后分别在交叉和变异操作中引入基因修复和多重验证机制,增强算法的寻优能力;最后给出了算法的总流程.算法在iMOPSE数据集上的求解效果表明本文的改进遗传算法是一种求解MSPSP问题的有效方法,对相关实际问题的研究具有良好借鉴意义.

    Abstract:

    In order to solve the Multi-Skilled resource-constrained Project Scheduling Problem (MSPSP), this study proposes an improved genetic algorithm. First, based on the mathematical model of the problem, a priority-based real number encoding method is established, and the objective function is converted into a fitness function for subsequent fitness calculations. Next, the niche technology based on group sharing is incorporated into the selection process of the genetic algorithm. In addition, with the help of deterministic sampling selection and subpopulation adjustment, the search ability of the algorithm is further improved. Then, gene repair and multiple verification mechanisms are introduced in the crossover and mutation operations to enhance the algorithm’s optimization ability. Finally, the overall process of the algorithm is given. The effect of the algorithm on the iMOPSE data set shows that the improved genetic algorithm is an effective method for solving MSPSP problem, and it has a sound reference significance for the study of related practical problems.

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宋尧,仰燕兰,叶桦.改进遗传算法在MSPSP问题中的验证.计算机系统应用,2020,29(10):235-241

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  • 收稿日期:2020-01-15
  • 最后修改日期:2020-02-13
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  • 在线发布日期: 2020-09-30
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