An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

Abstract

The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

“…The inertia weight, w is defined as follows [46,53]: where wmax and wmin represent the upper and lower limits of the inertia weight respectively. The parameter μ lies in the range [0, 1].…”

“…The parameter μ lies in the range [0, 1]. The acceleration coefficients are calculated at each iteration as follows [46]:…”

“…Maximum number of iterations (10) The hyperbolic tangent function is applied to δ to scale it to a range between 0 and 1. The function gives a smoother transition between the maximum and minimum inertia weight values as the iteration increases compared to the linearly decreasing inertia component alone [46,53] and the cosh function is applied to ψ to produce a smooth transition from the maximum acceleration coefficient Cmax to the minimum acceleration coefficient Cmin as the number of iterations increases. This gradual change in the values of the acceleration coefficients enables a controlled and stable optimization process, contributing to more reliable results [46].…”

“…The main objective of this paper is to use one of these variants developed by the authors to optimally tune the PID controllers for LFC of power systems integrated with renewable energy sources. This PSO variant called ADIWACO uses adaptive dynamic inertia weight and acceleration coefficients and it has proved to provide a better performance than the standard PSO and some PSO variants [46]. The proposed control strategy is tested on a two-area power system integrated with RES.…”