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Innovative approach for the determination of a DC motor’s and drive’s parameters using evolutionary methods and different measured current and angular speed responses
Marko Jesenik, Miha Ravber, Mislav Trbušić, 2024, izvirni znanstveni članek

Opis: The determination is presented of seven parameters of a DC motor’s drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters’ determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive’s acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the La calculation. The deviations of all the other parameters’ calculations were less than 2%.
Ključne besede: differential evolution, artificial bee colony, teaching-learning based optimization, DC motors, electric drive
Objavljeno v DKUM: 26.01.2024; Ogledov: 111; Prenosov: 8
.pdf Celotno besedilo (5,41 MB)

Variable-length differential evolution for numerical and discrete association rule mining
Uroš Mlakar, Iztok Fister, Iztok Fister, 2023, izvirni znanstveni članek

Opis: This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.
Ključne besede: association rule mining, differential evolution, data mining, variable-lenght solution representation, green AI
Objavljeno v DKUM: 18.01.2024; Ogledov: 147; Prenosov: 0

The 100-digit challenge : algorithm jDE100
Janez Brest, Mirjam Sepesy Maučec, Borko Bošković, objavljeni znanstveni prispevek na konferenci

Opis: Real parameter optimization problems are often very complex and computationally expensive. We can find such problems in engineering and scientific applications. In this paper, a new algorithm is proposed to tackle the 100-Digit Challenge. There are 10 functions representing 10 optimization problems, and the goal is to compute each function’s minimum value to 10 digits of accuracy. There is no limit on either time or the maximum number of function evaluations. The proposed algorithm is based on the self-adaptive differential evolution algorithm jDE. Our algorithm uses two populations and some other mechanisms when tackling the challenge. We provide the score for each function as required by the organizers of this challenge competition.
Ključne besede: differential evolution, optimization, global optimum, accuracy
Objavljeno v DKUM: 23.01.2023; Ogledov: 404; Prenosov: 15
.pdf Celotno besedilo (425,62 KB)
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Differential evolution and large-scale optimization applications
Aleš Zamuda, znanstveni film, znanstvena zvočna ali video publikacija

Opis: Differential Evolution (DE) is one of the most popular, high-performance optimization algorithms with variants that have been outperforming others for years. As a result, DE has grown to accommodate wide usage for a variety of disciplines across scientific fields. Differential Evolution and Large-Scale Optimization Applications presents a research-based overview and cross-disciplinary applications of optimization algorithms. Emphasizing applications of Differential Evolution (DE) across sectors and laying the foundation for further use of DE algorithms in real-world settings, this video is an essential resource for researchers, engineers, and graduate-level students. Topics Covered : Algorithms, Optimization, Parallel Differential Evolution, Performance Improvement, Stochastic Methods, Tree Model Reconstruction.
Ključne besede: differential Evolution, optimization, algorithms, stochastic methods, tree models, tree model reconstruction
Objavljeno v DKUM: 14.05.2019; Ogledov: 1609; Prenosov: 216
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Optimal robust motion controller design using multi-objective genetic algorithm
Andrej Sarjaš, Rajko Svečko, Amor Chowdhury, 2014, izvirni znanstveni članek

Opis: This paper describes the use of a multi-objective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with non-negativity conditions. Regional pole placement method is presented with the aims of controllers% structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multi-objective function is composed of different unrelated criteria such as, robust stability, controllers' stability and time performance indexes of closed loops. The design of controllers and multi-objective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm - Differential evolution.
Ključne besede: disturbance observer, DOB, uncertainty systems, optimal robust control, multi-objective optimization, differential evolution
Objavljeno v DKUM: 15.06.2017; Ogledov: 1405; Prenosov: 352
.pdf Celotno besedilo (2,22 MB)
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A hybrid bat algorithm
Iztok Fister, Dušan Fister, Xin-She Yang, 2013, izvirni znanstveni članek

Ključne besede: swarm intelligence, bat algorithm, differential evolution, optimization
Objavljeno v DKUM: 21.12.2015; Ogledov: 1941; Prenosov: 25
URL Povezava na celotno besedilo

Optimization of energy storage usage
Arnel Glotić, Peter Kitak, Igor Tičar, Adnan Glotić, 2012, objavljeni znanstveni prispevek na konferenci

Opis: This paper suggests the use of dynamic population size throughout the optimization process which is applied on the numerical model of a medium voltage post insulator. The main objective of the dynamic population is reducing population size, to achieve faster convergence. Change of population size can be done in any iteration by proposed method. The multiobjective optimization process is based on the PSO algorithm, which is suitably modifiedin order to operate with the principle of the optimal Pareto front.
Ključne besede: differential evolution, energy, optimization, hydro power plants
Objavljeno v DKUM: 10.07.2015; Ogledov: 1235; Prenosov: 43
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Performance comparison of self-adaptive and adaptive differential evolution algorithms
Janez Brest, Borko Bošković, Sašo Greiner, Viljem Žumer, Mirjam Sepesy Maučec, 2007, izvirni znanstveni članek

Opis: Differential evolution (DE) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization. for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, whichuse different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.
Ključne besede: differential evolution, control parameter, fitness function, optimization, self-adaption
Objavljeno v DKUM: 01.06.2012; Ogledov: 2097; Prenosov: 87
URL Povezava na celotno besedilo

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