School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610000, China.
Comput Intell Neurosci. 2018 Sep 2;2018:4617816. doi: 10.1155/2018/4617816. eCollection 2018.
There are several intelligent algorithms that are continually being improved for better performance when solving the flexible job-shop scheduling problem (FJSP); hence, there are many improvement strategies in the literature. To know how to properly choose an improvement strategy, how different improvement strategies affect different algorithms and how different algorithms respond to the same strategy are critical questions that have not yet been addressed. To address them, improvement strategies are first classified into five basic improvement strategies (five structures) used to improve invasive weed optimization (IWO) and genetic algorithm (GA) and then seven algorithms (S1-S7) used to solve five FJSP instances are proposed. For the purpose of comparing these algorithms fairly, we consider the total individual number (TIN) of an algorithm and propose several evaluation indexes based on TIN. In the process of decoding, a novel decoding algorithm is also proposed. The simulation results show that different structures significantly affect the performances of different algorithms and different algorithms respond to the same structure differently. The results of this paper may shed light on how to properly choose an improvement strategy to improve an algorithm for solving the FJSP.
有几种智能算法不断得到改进，以在解决柔性作业车间调度问题 (FJSP) 时获得更好的性能；因此，文献中有许多改进策略。要知道如何正确选择改进策略、不同的改进策略如何影响不同的算法以及不同的算法如何响应相同的策略，这些都是尚未解决的关键问题。为了解决这些问题，首先将改进策略分为用于改进入侵杂草优化 (IWO) 和遗传算法 (GA) 的五种基本改进策略（五种结构），然后提出了用于解决五个 FJSP 实例的七种算法（S1-S7）。为了公平地比较这些算法，我们考虑了算法的总个体数 (TIN)，并基于 TIN 提出了几个评估指标。在解码过程中，还提出了一种新的解码算法。仿真结果表明，不同的结构对不同算法的性能有显著影响，而不同的算法对相同的结构的响应也不同。本文的结果可能为如何正确选择改进策略以改进用于解决 FJSP 的算法提供一些启示。
