Browsing by Subject "Particle Swarm Optimization"

Now showing 1 - 4 of 4

Access status: Open Access ,
Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms
(Wydawnictwa AGH, 2022) Olejarczyk-Wożeńska, Izabela; Opaliński, Andrzej; Mrzygłód, Barbara; Regulski, Krzysztof; Kurowski, Wojciech
The paper presents the application of heuristic optimization methods in identifying the parameters of a model for bainite transformation time in ADI (Austempered Ductile Iron). Two algorithms were selected for parameter optimization - Particle Swarm Optimization and Evolutionary Optimization Algorithm. The assumption of the optimization process was to obtain the smallest normalized mean square error (objective function) between the time calculated on the basis of the identified parameters and the time derived from the experiment. As part of the research, an analysis was also made in terms of the effectiveness of selected methods, and the best optimization strategies for the problem to be solved were selected on their basis.
Access status: Restricted ,
Electron microscopy image segmentation using swarm intelligence
(Data obrony: 2020-09-29) Nawrot, Weronika
Wydział Energetyki i Paliw
Access status: Open Access ,
Modelling Microcystis cell density in a mediterranean shallow lake of northeast Algeria (Oubeira Lake), using evolutionary and classic programming
(Wydawnictwa AGH, 2023) Arif, Salah; Djellal, Adel; Djebbari, Nawel; Belhaoues, Saber; Touati, Hassen; Guellati, Fatma Zohra; Bensouilah, Mourad
Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed, MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
Access status: Restricted ,
Stochastic Recursive Algorithms application in tensor distance optimization
(Data obrony: 2017-10-06) Kozubal, Adam
Wydział Geologii, Geofizyki i Ochrony Środowiska
Presented thesis describes Stochastic Recursive Algorithms application in a tensor distance optimization. Problem of the effective tensor evaluation was chosen as a real scenario for tensor distance optimization. For the purpose of this studies application was written in C language under Linux OS. What is more author implemented and discussed own algorithm to solves presented problem.