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Statistical Performances Evaluation of APSO and Improved APSO for Short Term Hydrothermal Scheduling Problem

1
Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
2
Power & Mechanical Division, National Engineering Services Pakistan (NESPAK) Pvt. Limited, Lahore 54000, Pakistan
3
Sharif College of Engineering and Technology, Lahore, Pakistan
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Department of Electrical Engineering, University of Engineering and Technology, Taxila, Pakistan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(12), 2440; https://doi.org/10.3390/app9122440
Received: 22 April 2019 / Revised: 10 June 2019 / Accepted: 11 June 2019 / Published: 14 June 2019
The Accelerated Particle Swarm Optimization (APSO) algorithm is an efficient and the easiest to implement variant of the famous Particle Swarm Optimization (PSO) algorithm. PSO and its variant APSO have been implemented on the famous Short-Term Hydrothermal Scheduling (STHTS) problem in recent research, and they have shown promising results. The APSO algorithm can be further modified to enhance its optimizing capability by deploying dynamic search space squeezing. This paper presents the implementation of the improved APSO algorithm that is based on dynamic search space squeezing, on the short-term hydrothermal scheduling problem. To give a quantitative comparison, a true statistical comparison based on comparing means is also presented to draw conclusions. View Full-Text
Keywords: improved APSO; dynamic search space squeezing; independent sample t-test improved APSO; dynamic search space squeezing; independent sample t-test
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Fakhar, M.S.; Kashif, S.A.R.; Ain, N.U.; Hussain, H.Z.; Rasool, A.; Sajjad, I.A. Statistical Performances Evaluation of APSO and Improved APSO for Short Term Hydrothermal Scheduling Problem. Appl. Sci. 2019, 9, 2440.

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