Wyniki wyszukiwania - BazTech

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Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming

Arif Salah, Djellal Adel, Djebbari Nawel, Belhaoues Saber, Touati Hassen, Guellati Fatma Zohra, Bensouilah Mourad

Geomatics and Environmental Engineering

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2023

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Vol. 17, no. 2

31--68

EN

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.

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Maintenance policy of degradation components based on the two-phase Wiener process

Song Minqiao, Zhang Yingzhi, Yang Fang, Wang Xiaofeng, Guo Guiming

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 4

art. no. 172537

EN

This paper proposes a condition-based maintenance policy for the two-phase Wiener degradation process components. The main contribution of this article is to provide the time distribution of degradation failures for the two-phase Wiener process degradationcomponent, as well as the modeling and solving methods for two-phase maintenance. The two-phase maintenance policy includes two-phase inspection and preventive replacement maintenance operations. The established optimization maintenance policy model aims to minimize long-term operation costs. The specific cost calculation equation and the solution method of the maintenance model are given. The feasibility of the maintenance policy model is verified using the two-phase degradation data of the Liquid Coupling Devices. The Particle swarm optimization algorithm can stably solve the described problem, and the results show that the two-phase maintenance policy can be more economical and improve components availability. After that, we also analyzed the impact of the cost parameters on the maintenance policy through sensitivity analysis.

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Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms

Olejarczyk-Wożeńska Izabela, Opaliński Andrzej, Mrzygłód Barbara, Regulski Krzysztof, Kurowski Wojciech

Computer Methods in Materials Science

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2022

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Vol. 22, No. 3

125--136

EN

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.

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A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm

Dziwiński Piotr, Bartczuk Łukasz, Paszkowski Józef

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 2

95--111

EN

The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.

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Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization Framework

Zaborski Mateusz, Okulewicz Michał, Mańdziuk Jacek

Foundations of Computing and Decision Sciences

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2020

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Vol. 45, No. 3

234--254

EN

This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper. We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.