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system using Particle Swarm Optimization (PSO) is introduced, which is implemented using Matlab. To solve a particle optimization problem, the number of particles is often set between 10 and 50. At first, the particle positions are - initialized randomly

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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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] 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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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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Pollack Periodica
Authors:
Ali Kareem Abdulrazzaq
,
György Bognár
, and
Balázs Plesz

. This paper uses the particle swarm optimization algorithm to solve this equation and determine the five parameters. PSO is one of the well-regarded algorithms in the literature for optimization tasks and is widely used in both science and industry. PSO

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changing solar irradiation and partial shade [ 3 ]. The literature has reviewed evolutionary algorithms Adaptive Neuro-Fuzzy Inference System (ANFIS-MPPT), Particle Swarm Optimization (PSO) differential evolution algorithm, Firefly Algorithm (FA) and

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) and Particle Swarm Optimization (PSO) is presented. In [ 7 ], Generalized Normal Distribution algorithm (GNDA), and in [ 8 ], Firefly Optimization Algorithm (FOA) along with GA are implemented to achieve better V dc

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. Comparison of ARGA results with various approaches for test case A Variables PSO RSM SA BA ARGA Δ P G 1 −8.61 −8.808 −9.076 −9.010 −8.80 Δ P G 2 10.40 2.647 3.133 13.969 15.10 Δ P G 3 3.03 2.953 3.234 0.102 0.00 Δ P G 4 0.02 3.063 2.968 0.301 0.10 Δ P G 5 0

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cloud using the metaheuristic method. Some researchers [ 6 ] proposed a Particle Swarm Optimization (PSO) based method to minimize global costs of the workflows scheduling in the cloud, which are transmission and execution cost. Several

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drilling data was used from Khangiran field to calculate the difference between the actual penetration rate and the predicted one by Particle Swarm Optimization (PSO) [ 9 ], Dynamic Differential Annealing Optimization, (DDAO) [ 10 ], Artificial Bee Colony

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