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首页> 外文会议>International Conference on New Trends in Information Science, Service Science and Data Mining >Hybrid Multi-Objective PSO with Solution Diversity Extraction for Job-Shop Scheduling Management
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Hybrid Multi-Objective PSO with Solution Diversity Extraction for Job-Shop Scheduling Management

机译：杂交多目标PSO与求职店调度管理的解决方案多样性提取

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The Multi-Objective Flexible Job-Shop Scheduling Problem (FJSP), which concerned with allocating limited resources to optimize some performance criteria, is difficult to find optimal scheduling solutions because of NP-hard complexity. In this paper, the particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) is proposed to always produce feasible candidate solutions for the FJSP. Then a solution searching strategy called Solution Diversity Extraction is adopted to improve the Particle Swarm Optimization (PSO) to deal with the diversity in Pareto-optimal solutions. To test the performance of the proposed method, the experiments contain six representative benchmarks and to compare the proposed method with the published algorithms. The simulation results indicate the proposed method can find more wide range potential solutions, and outperform related methods.
机译：涉及分配有限资源的多目标灵活作业商店调度问题（FJSP），以优化一些性能标准，很难找到最佳的调度解决方案，因为没有NP - 硬复杂性。在本文中，提出了名为粒子段操作机器分配（PSOMA）的粒子编码表示，以始终为FJSP产生可行的候选解决方案。然后采用一种调用解决方案分集提取的解决方案搜索策略来改善粒子群优化（PSO）以处理静态解决方案中的多样性。为了测试所提出的方法的性能，实验包含六种代表性基准，并将提出的方法与已发布的算法进行比较。仿真结果表明，所提出的方法可以找到更广泛的范围潜在的解决方案，并且优异的相关方法。

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