Iskanje po katalogu digitalne knjižnice

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: 123; Prenosov: 9
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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: 1061; Prenosov: 339
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Ključne besede: matematika, diferencialne enačbe, evolucijske enačbe, mathematics, differential equations, evolution equations, regularized evolution families
Objavljeno v DKUM: 10.07.2015; Ogledov: 925; Prenosov: 17
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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: 1016; Prenosov: 41
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Opis: This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually reducing population size is proposed in this paper. It improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm. The proposed modification is tested on commonly used benchmark problems for unconstrained optimization and compared with other optimization methods such as Evolutionary Algorithms and Evolution Strategies.
Ključne besede: differential evolution, control parameter, fitness function, global function optimization, self-adaptation, population size
Objavljeno v DKUM: 01.06.2012; Ogledov: 1996; Prenosov: 123
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