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首页> 外文期刊>International Journal of Innovative Computing Information and Control >EVOLUTIONARY ALGORITHMS TO SOLVE LOOSELY CONSTRAINED PERMUT-CSPS: A PRACTITIONERS APPROACH
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EVOLUTIONARY ALGORITHMS TO SOLVE LOOSELY CONSTRAINED PERMUT-CSPS: A PRACTITIONERS APPROACH

机译：解决松散约束的CSPS的进化算法：从业人员方法

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Permutation constraint satisfaction problems (permut-CSPs) can be found in many practical applications, wherein most instances usually have a low density of constraints. This paper explores two evolutionary approaches to solve this kind of problem from a practical perspective. A test case that captures the main characteristics present in real world applications is used to design and test the performance of a PSO agent and a GA agent, which are also systematically tuned to determine the best configurations using statistical analysis. We conclude that the PSO agent provides a better fit to the nature of loosely constrained permut-CSPs, resulting in better performance. This paper focuses on the trade-off between development costs (including tuning) and the performance of both evolutionary algorithms, and aims to help practitioners choose the best algorithm and configuration for these problems.
机译：置换约束满足问题（permut-CSP）可以在许多实际应用中找到，其中大多数实例通常具有较低的约束密度。本文从实践的角度探讨了两种解决此类问题的进化方法。捕获现实世界应用程序中存在的主要特征的测试用例用于设计和测试PSO代理和GA代理的性能，并使用统计分析对它们进行系统地调整以确定最佳配置。我们得出结论，PSO代理可以更好地适应松散约束的置换CSP的性质，从而获得更好的性能。本文着重在开发成本（包括调整）和两种进化算法的性能之间进行权衡，旨在帮助从业人员为这些问题选择最佳的算法和配置。

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