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

Muhammad Salman Fakhar,Noor Ul Ain,Akhtar Rasool,Hafiz Zaheer Hussain,Intisar Ali Sajjad,Syed Abdul Rahman Kashif

doi:10.3390/app9122440

Abstract

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.

Highlights

The Particle Swarm Optimization (PSO) algorithm has gained much popularity among the metaheuristic optimization algorithms in the recent past due to its ease of implementation and promise towards finding good approximates to global optimum solutions of complex optimization problems [1]
The work in [25] has applied the concept of the dynamic search squeezing technique on the canonical form of PSO, i.e., on Equation (1), on the velocity update equation. These equations help to squeeze the search space dynamically from the given constraints to newer constraints from one iteration to the iteration. This paper presents this concept of dynamic search space squeezing on the Accelerated Particle Swarm Optimization (APSO) algorithm, i.e., Equation (2), to further enhance its performance
The work in [24] has already discussed the implementation of APSO on this problem; this paper presents its comparison with the improved APSO algorithm on hypothetical testing

Summary

Introduction

The PSO algorithm has gained much popularity among the metaheuristic optimization algorithms in the recent past due to its ease of implementation and promise towards finding good approximates to global optimum solutions of complex optimization problems [1]. APSO is a simpler yet brilliant variant of PSO, and it has be proven to find good approximates of global optimum solutions in less time and fewer iterations as compared to PSO algorithms [2]. Short-term hydrothermal scheduling is a non-linear and multi-modal optimization problem, which has many forms. These can be of a non-cascaded form to a cascaded form. In the non-cascaded form, there is only one reservoir of water, whereas in the cascaded form, there is a series of downstream reservoirs

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Results

Conclusion