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

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 resea...

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Bibliographic Details
Published in:Applied sciences 2019-06, Vol.9 (12), p.2440
Main Authors: Fakhar, Muhammad Salman, Kashif, Syed Abdul Rahman, Ain, Noor Ul, Hussain, Hafiz Zaheer, Rasool, Akhtar, Sajjad, Intisar Ali
Format: Article
Language:English
Subjects:
Algorithms
dynamic search space squeezing
Genetic algorithms
Gravitation
Gravity
Hypotheses
Hypothesis testing
improved APSO
independent sample t-test
Multiple objective analysis
Optimization
Optimization algorithms
Random variables
Scheduling
Search algorithms
Short term
Student's t-test
Velocity
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9122440