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Modified genetic algorithm with sampling techniques for chemical engineering optimization

机译:带有采样技术的改进遗传算法用于化学工程优化

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摘要

In this work, we develop a new efficient technique to enhance the optimization ability, and to improve the convergence speed of genetic optimization algorithm. We investigate and introduce a number of sampling techniques to generate a good set of initial population that encourages the exploration through out the search space and hence achieves better discovery of possible global optimum in the solution space. The introduced sampling techniques include Latin hypercube sampling (LHS), Faure sequence sampling (FSS), and Hammersley sequence sampling (HSS). The performances of the proposed algorithms and a conventional genetic algorithm using uniformly random population are compared, both in terms of solution quality and speed of convergence. A number of test problems and a case study, optimization of multi-effect distillation, demonstrate the feasibility and effectiveness of the proposed techniques. With the same parameters, our technique provides a better solution and converge to the global optimum faster than the traditional genetic algorithm.
机译:在这项工作中,我们开发了一种新的有效技术来增强优化能力,并提高遗传优化算法的收敛速度。我们研究并介绍了许多采样技术,以生成一组良好的初始种群,这些种群鼓励在整个搜索空间中进行探索,从而更好地发现解空间中可能存在的全局最优值。引入的采样技术包括拉丁超立方体采样(LHS),福尔序列采样(FSS)和哈默斯利序列采样(HSS)。从解决方案质量和收敛速度两方面,比较了所提出的算法和使用均匀随机总体的常规遗传算法的性能。大量的测试问题和案例研究,多效蒸馏的优化证明了所提出技术的可行性和有效性。在相同参数的情况下,我们的技术提供了更好的解决方案,并且比传统的遗传算法更快地收敛到全局最优。

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