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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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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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