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参数控制对 APSO 和 PSO 算法在 CSTHTS 问题中性能的影响：算法结构和结果的改进。

Impact of parameter control on the performance of APSO and PSO algorithms for the CSTHTS problem: An improvement in algorithmic structure and results.

机构信息

Department of Electrical Engineering, University of Engineering and Technology, Lahore, Punjab, Pakistan.

Department of Electrical Engineering, Sharif College of Engineering and Technology, Lahore, Punjab, Pakistan.

出版信息

PLoS One. 2021 Dec 17;16(12):e0261562. doi: 10.1371/journal.pone.0261562. eCollection 2021.

DOI:10.1371/journal.pone.0261562
PMID:34919600
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8682890/
Abstract

Cascaded Short Term Hydro-Thermal Scheduling problem (CSTHTS) is a single objective, non-linear multi-modal or convex (depending upon the cost function of thermal generation) type of Short Term Hydro-Thermal Scheduling (STHTS), having complex hydel constraints. It has been solved by many metaheuristic optimization algorithms, as found in the literature. Recently, the authors have published the best-achieved results of the CSTHTS problem having quadratic fuel cost function of thermal generation using an improved variant of the Accelerated PSO (APSO) algorithm, as compared to the other previously implemented algorithms. This article discusses and presents further improvement in the results obtained by both improved variants of APSO and PSO algorithms, implemented on the CSTHTS problem.

摘要

级联短期水电调度问题 (CSTHTS) 是一个单目标、非线性多模态或凸型（取决于热能发电的成本函数）的短期水电调度 (STHTS) 问题，具有复杂的水电约束。许多元启发式优化算法已经对其进行了解决，这些算法在文献中都有记载。最近，作者使用改进的加速粒子群优化算法 (APSO) 对热能发电的二次燃料成本函数的 CSTHTS 问题进行了研究，并公布了该问题的最佳结果，与之前实现的其他算法相比，该算法取得了最佳的结果。本文讨论并提出了在 CSTHTS 问题上实现的改进型 APSO 和 PSO 算法的结果的进一步改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/4dd6cbbcb36b/pone.0261562.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/2e9aa88799f1/pone.0261562.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/9045a34f0f28/pone.0261562.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/5b0fb1732f32/pone.0261562.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/4dd6cbbcb36b/pone.0261562.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/2e9aa88799f1/pone.0261562.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/9045a34f0f28/pone.0261562.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/5b0fb1732f32/pone.0261562.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8b8/8682890/4dd6cbbcb36b/pone.0261562.g004.jpg

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