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International Review of Applied Sciences and Engineering
Authors:
Ammar Al-Jodah
,
Saad Jabbar Abbas
,
Alaq F. Hasan
,
Amjad J. Humaidi
,
Abdulkareem Sh. Mahdi Al-Obaidi
,
Arif A. AL-Qassar
, and
Raaed F. Hassan

that involves the proposed control methodology is shown in Fig. 2 . The error between the reference and actual position is multiplied by the orthogonal rotational matrix to transfer the error from the world coordinates to the robot coordinates. The PSO

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error value. It is essential to adjust the PID parameters to obtain the desired output. For this purpose, various optimization algorithms are used. One of them is particle swarm optimization (PSO) [ 1 , 2 ]. PID is used in many applications such as

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by PSCs [ 17 ]. However, the development of MPPT controllers has also made use of optimization techniques that are based on swarm intelligence (SI) [ 18 , 19 ], like ant colony optimization (ACO) and particle swarm optimization (PSO) [ 20 , 21

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inverse kinematics (IK) of a 3-DOF redundant manipulator robot. Batista et al. [ 15 ] used Least Squares (LS), Recursive Least Square (RLS), and dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined

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Optimization (PSO); Ant Colony Optimization (ACO.); and Genetic Algorithm (GA); are commonly employed (GA). Despite its advantages over other artificial intelligence algorithms, as demonstrated by the results of this research, the SSO is rarely employed to

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-copter. ❑ Conducting a comparative study between SHO and PSO algorithms in terms of dynamic performance and robustness characteristics. The rest of the article is arranged as follows: Section 2 developed the mathematical model of Tri-copter. Section 3 presents the

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and complicated calculation [ 7 ]. This incorporation of Particle Swarm Optimization (PSO) not only improved the prediction accuracy of the neural network model but also contributed to a more robust and precise characterization of the asphalt foaming

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Pollack Periodica
Authors:
Humam Kareem Jalghaf
,
Ali Habeeb Askar
,
Hazim Albedran
,
Endre Kovács
, and
Károly Jármai

Optimization (ALO) algorithm [ 8 ]. The behavior of the flocks of birds and fishes was the inspiration engine behind the Particle Swarm Optimization (PSO) algorithm, while the bee colony was the inspiration engine behind the Artificial Bee Colony (ABC) [ 9

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] methods; this term can be defined as accumulative and shared knowledge among a group of individuals, and this kind of intelligence cannot be reached by one of them alone. Examples of swarm intelligence Particle Swarm Optimization (PSO) [ 6 ], Artificial

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by congestion [ 6 ]. Nodal pricing, FACTS-based CM, ATC-based CM, and price area CM methods are coming under conventional CM methods. The computational time is more in Conventional CM methods compared to heuristic optimization methods. PSO, GA, BFA

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