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

• Main Authors: Muhammad Salman Fakhar, Syed Abdul Rahman Kashif, Noor Ul Ain, Hafiz Zaheer Hussain, Akhtar Rasool, Intisar Ali Sajjad
• Format: Article
• Language: English
• Published: MDPI AG 2019-06-01
• Series: Applied Sciences
• Subjects: improved APSO; dynamic search space squeezing; independent sample <i>t</i>-test
• Online Access: https://www.mdpi.com/2076-3417/9/12/2440
• ISSN: 2076-3417

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.