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have gained popularity in optimization and AI for their ability to handle complex real-world challenges where traditional methods may struggle. The optimization technique known as Particle Swarm Optimization (PSO) draws inspiration from social behaviors

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efficiency and security of a scrambler. In the proposed scheme, the particle swarm optimization algorithm generates the optimum permutation array that attains the highest residual intelligibility in the scrambled speech. This invention aims to improve

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limiting the number of variant permutations, which reduces the security. To address this problem, the proposed approach combines multiwavelet, Arnold Transforms (AT), and Particle swarm optimization to create a mixed transformation. The Particle swarm

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Particle Swarm Optimization (PSO) algorithm, a heuristic optimization method, to estimate the parameters that define the impact zone on a beam. This method was specifically designed to accurately locate a non-punctual impact zone. To overcome these

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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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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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. sec 2 Ground acceleration g 9.81 m / s e s 2 The reference trajectory is defined as: x d = sin ( p i 20 t ) , y d = sin ( p i 20 t ) , z d = 2 , ψ d = 0 The particle swarm optimization (PSO) has been applied as competitive optimization method. The

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active power is rescheduled to alleviate the congestion. Using the multi-objective Particle Swarm Optimization (PSO) algorithm, part-optimal solutions were introduced by Hazara and Sinha [ 3 ] to minimize overloads and lower operating costs. Balaraman et

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concrete retaining walls, optimization techniques are utilized, making retaining wall optimization essential for achieving economic efficiency [ 8 ]. In this work, Particle Swarm Optimization (PSO) [ 9 ], Grey Wolf Optimizer (GWO) [ 10 ], Artificial Bee

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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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