Particle Swarm Optimization is an optimization algorithm that is often used and has been proven to improve the results of a clustering. In this case, optimization using Particle Swarm Optimization (PSO) in the selection of the initial cluster center needs to be applied to the k...
This research proposes EDAPSO, an algorithm which consists of hybridization of EDA and Particle Swarm Optimization (PSO). The objective of this research is to test the performance of EDAPSO algorithm for solving PFSB. EDAPSO's performance is tested in 10 benchmark problems of PFSB and it ...
In addition, the paper utilizes two search algorithms (Grid Search, Coarse to Fine Search) with three meta-optimization methods (Standard GA, Particle Swarm Optimization, GA-BMT) to investigate the best parameter settings of the Standard GA for 17 test functions, and offe...
ANALISIS PERBANDINGAN ALGORITMA PARTICLE SWARM OPTIMIZATION DAN FIREFLY ALGORITHM DALAM MENENTUKAN MINIMUM SPANNING TREEdoi:10.31869/RTJ.V1I2.723Desi NoviantiDewi Anggraini Puspa HapsariMuhammadiyah University West Sumatra
The CSGA algorithm used in the calculation of collision prevention routes and the ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm) algorithms previously used in the literature were also used for calculation and the results compare...
While minimizing is achieved in the binary particle swarm optimization algorithm, the random forest algorithm is used in the fitness function. With feature selection, it is aimed to increase speed and performance by reducing computational load on classification algorithm. In prop...
PARTICLE swarm optimizationMETAHEURISTIC algorithmsPhotovoltaic systems are one of the renewable energy systems that convert solar radiation directly into electricity. The relationship between current and voltage of PV system is nonlinear and it has only one point where power efficiency...
In the study, the methods of Genetic Algorithm, Particle Swarm Optimization and Artificial Bee Colony, which are accepted as evolutionary calculation techniques at the point of solution of the problem, were used and the performance of the existing methods on the solution of the problem wa...
Annealing Simulation, Particle Swarm Optimization and Ant Colony Algorithms have been used to solve the travelling salesman problem, and their performances have been examined. When comparing the test results of the obtained models, it is observed that the Ant Colony Algorithm, ...
In this study, an algorithm that combines the advantages of Bat and particle swarm optimization (PSO) algorithm is proposed to solve localization problems in wireless sensor networks. The result shows that the Bat-PSO hybrid algorithm is able to estimate all node positions in variou...