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首页> 外文期刊>Applied Artificial Intelligence >AN ANALYSIS OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR OPTIMIZATION PROBLEMS WITH TIME CONSTRAINTS
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AN ANALYSIS OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR OPTIMIZATION PROBLEMS WITH TIME CONSTRAINTS

机译：时间约束最优化问题的多目标进化算法分析

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摘要

Many multiobjeclive optimization problems in the engineering field are required lo be solved within more or less severe time restrictions. Because the optimization criteria, the parameters, and/ or constraints might change with time, the optimization solutions must be recalculated when a change takes place. The lime required by the optimization procedure to arrive at the new solutions should be bounded accordingly with the rale of change observed in these dynamic problems. This way, the faster the optimization algorithm is lo obtain solutions, the wider is the set of dynamic problems lo which that algorithm can be applied. Here, we analyze the performance of the nondominaled sorting algorithm (NSGA-II), strength Parelo evolutionary algorithm (SPEA2), and single front genetic algorithms (SFGA, and SFGA2) on two different multiobjeclive optimization problems, with two and three objectives, respectively. For these two studied problems, the single front genetic algorithms have obtained adequate quality in the solutions in very little lime. Moreover, for the second and more complex problem approached, SFGA2 and NSGA-II obtain the best hypervolume in the found set of nondominaled solutions, but SFGA2 employs much less lime than NSGA-II. These results may suggest that single front genetic algorithms, especially SFGA2, could be appropiale lo deal with optimization problems with high rales of change, and thus stronger lime constraints.
机译：需要在或多或少的严格时间限制内解决工程领域的许多多目标优化问题。由于优化标准，参数和/或约束可能会随时间变化，因此，当发生更改时，必须重新计算优化解决方案。优化程序达到新解决方案所需的时间应该相应地与在这些动态问题中观察到的变化范围相联系。这样，优化算法获得解决方案的速度越快，可以应用该算法的动态问题的范围就越广。在这里，我们分析了非归类排序算法（NSGA-II），强度Parelo进化算法（SPEA2）和单前沿遗传算法（SFGA和SFGA2）在两个不同的具有两个目标和三个目标的多目标优化问题上的性能。 。对于这两个已研究的问题，单前遗传算法在很少的石灰中就获得了足够的质量。此外，对于所解决的第二个更复杂的问题，SFGA2和NSGA-II在发现的非标定解集中获得了最佳的超体积，但是SFGA2所用的石灰比NSGA-II少得多。这些结果可能表明，单前遗传算法，尤其是SFGA2，可以适当地处理具有高变化规则并因此具有更强的石灰约束条件的优化问题。

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  • 来源
    《Applied Artificial Intelligence》 |2013年第10期|851-879|共29页
  • 作者

    M. Camara; F. de Toro; J. Ortega;

  • 作者单位

    Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;

    Signal Theory, Networking and Communications Department, University of Granada, C/Periodista Daniel Saucedo Aranda s, 18071, Granada, Spain;

    Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;

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