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A new multiobjective genetic algorithm with heterogeneous population for solving flowshop scheduling problems

机译：一种新的具有异构种群的多目标遗传算法，用于解决Flowshop调度问题

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摘要

The present paper discusses the application of a new genetic algorithm (GA) featuring heterogeneous population to solve multiobjective flowshop scheduling problems. Many GAs have been developed to solve multiobjective scheduling problems, but they used a non-heterogeneous population approach, which could lead to premature convergence and local Pareto-optimum solutions. Our experiments with a 20-job and 20-machine benchmark problem given in Taillard (1993) show that the heterogeneous multiobjective genetic algorithm (hMGA) developed in this research outperforms NSGA-II (Deb 2001) one of the widely used algorithms with non-heterogeneous population. Moreover, in this paper we also present the comparison of hMGA with another meta-heuristic method, i.e. multi-objective simulated annealing (MOSA), proposed by Varadharajan and Rajendran (2005). This research concludes that hMGA developed in this work is promising as it can produce a new set of Pareto-optimum solutions that have not been found by MOSA before.
机译：本文讨论了一种具有异质种群的新遗传算法（GA）在解决多目标Flowshop调度问题中的应用。已经开发了许多遗传算法来解决多目标调度问题，但是它们使用了非异构种群方法，这可能导致过早收敛和局部帕累托最优解。我们针对Taillard（1993）中的20个工作和20个机器的基准问题进行的实验表明，本研究中开发的异构多目标遗传算法（hMGA）优于NSGA-II（Deb 2001），后者是一种广泛使用的非遗传算法。异类人口。此外，在本文中，我们还介绍了hMGA与另一种启发式方法的比较，该方法是Varadharajan和Rajendran（2005）提出的多目标模拟退火（MOSA）。这项研究得出的结论是，这项工作开发的hMGA具有广阔的前景，因为它可以产生一组新的Pareto优化解决方案，而这是MOSA以前找不到的。

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