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From an Optimal Point to an Optimal Region: A Novel Methodology for Optimization of Multimodal Constrained Problems and a Novel Constrained Sliding Particle Swarm Optimization Strategy
Mathematics ( IF 2.4 ) Pub Date : 2021-07-30 , DOI: 10.3390/math9151808
Carine M. Rebello , Márcio A. F. Martins , José M. Loureiro , Alírio E. Rodrigues , Ana M. Ribeiro , Idelfonso B. R. Nogueira

The present work proposes a novel methodology for an optimization procedure extending the optimal point to an optimal area based on an uncertainty map of deterministic optimization. To do so, this work proposes the deductions of a likelihood-based test to draw confidence regions of population-based optimizations. A novel Constrained Sliding Particle Swarm Optimization algorithm is also proposed that can cope with the optimization procedures characterized by multi-local minima. There are two open issues in the optimization literature, uncertainty analysis of the deterministic optimization and application of meta-heuristic algorithms to solve multi-local minima problems. The proposed methodology was evaluated in a series of five benchmark tests. The results demonstrated that the methodology is able to identify all the local minima and the global one, if any. Moreover, it was able to draw the confidence regions of all minima found by the optimization algorithm, hence, extending the optimal point to an optimal region. Moreover, providing the set of decision variables that can give an optimal value, with statistical confidence. Finally, the methodology is evaluated to address a case study from chemical engineering; the optimization of a complex multifunctional process where separation and reaction are processed simultaneously, a true moving bed reactor. The method was able to efficiently identify the two possible optimal operating regions of this process. Therefore, proving the practical application of this methodology.

中文翻译:

从最佳点到最佳区域:一种优化多模态约束问题的新方法和一种新的约束滑动粒子群优化策略

目前的工作提出了一种优化程序的新方法,该方法基于确定性优化的不确定性图将最佳点扩展到最佳区域。为此,这项工作提出了基于似然的测试的推论,以绘制基于人口的优化的置信区域。还提出了一种新的约束滑动粒子群优化算法,可以应对以多局部最小值为特征的优化过程。优化文献中有两个未解决的问题,确定性优化的不确定性分析和元启发式算法在解决多局部最小值问题中的应用。所提出的方法在一系列五个基准测试中进行了评估。结果表明,该方法能够识别所有局部最小值和全局最小值,如果有的话。此外,它能够绘制优化算法找到的所有最小值的置信区域,从而将最佳点扩展到最佳区域。此外,提供一组可以给出最优值的决策变量,具有统计置信度。最后,评估该方法以解决化学工程的案例研究;一个复杂的多功能过程的优化,其中分离和反应同时进行,一个真正的移动床反应器。该方法能够有效地识别该过程的两个可能的最佳操作区域。因此,证明了这种方法的实际应用。提供一组可以给出最优值的决策变量,具有统计置信度。最后,评估该方法以解决化学工程的案例研究;一个复杂的多功能过程的优化,其中分离和反应同时进行,一个真正的移动床反应器。该方法能够有效地识别该过程的两个可能的最佳操作区域。因此,证明了这种方法的实际应用。提供一组可以给出最优值的决策变量,具有统计置信度。最后,评估该方法以解决化学工程的案例研究;一个复杂的多功能过程的优化,其中分离和反应同时进行,一个真正的移动床反应器。该方法能够有效地识别该过程的两个可能的最佳操作区域。因此,证明了这种方法的实际应用。该方法能够有效地识别该过程的两个可能的最佳操作区域。因此,证明了这种方法的实际应用。该方法能够有效地识别该过程的两个可能的最佳操作区域。因此,证明了这种方法的实际应用。
更新日期:2021-07-30
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