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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Afrah Abood Abdul Kadhim
Southern Technical University
Economics, Econometrics & Finance Electrical & Electronics Engineering

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Using particle swarm optimization to solve test functions problems Issa Ahmed Abed; May Mohammed Ali; Afrah Abood Abdul Kadhim
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3244

Abstract

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
Design of a solar system with a PID controller based on the Tyrannosaurus optimization algorithm Kadhim Sabah Rahimah; Issa Ahmed Abed; Afrah Abood Abdul Kadhim
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp170-182

Abstract

Although photovoltaic (PV) power generation systems are an efficient way to use solar energy, their conversion efficiency is very low. Keeping the DC output power from the panel consistent is the key challenge with solar PV systems. Radiation and temperature are two variables that can impact a panel's output power. This study proposes a unique hunting-based optimization technique called the Tyrannosaurus optimization algorithm (TROA). It is demonstrated that the TROA can be used to achieve maximum power point tracking (MPPT) for lithium-ion battery charging with solar panels. Tyrannosaurus Rex hunting techniques served as the model for this approach. MPPT is used to regulate the solar array's output in PV systems. A buck converter is used by the charge controller to convert DC to DC. To provide the most power, it is utilized to balance the impedance of batteries and solar panels. To maximize power transfer, the algorithm modifies the gating signal's duty cycle based on the voltage and current detected by the solar panel. Three well-known optimization methods are contrasted with TROA's performance: gorilla troops optimization (GTO) algorithm, perticle swarm optimization (PSO), and cultural algorithm (CA). In contrast to current approaches, the proposed approach has yielded superior results.
Co-Authors Issa Ahmed Abed Kadhim Sabah Rahimah May Mohammed Ali