Wyniki wyszukiwania - Biblioteka Nauki

1

On two models for global optimization

100%

Żilinskas A.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2007

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tom z. 160

9-16

EN

Two models for global optimization are considered: statistical model, and radial basis functions. The equivalence of both models in the case of optimization without noise is discussed. Both models are also evaluated with respect to global optimization in the presence of noise by means of experimental testing where approximation errors of passive one dimensional algorithm are estimated.

2

Evolutionary algorithms with mutations based on (alfa) Stable and directional distributions

100%

Obuchowicz A.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2007

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tom z. 160

209-216

EN

In this paper a concept of directional mutations for phenotypic evolutionary algorithms is presented. The proposed approach allows, in a very convenient way, to adapt the probability measure underlying the mutation operator during evolutionary process. Simulated experiments confirms the thesis that proposed mutation improves the effectiveness of evolutionary algorithms in the case of the local as well as global optimization problems.

3

Evolutionary Computation for Global Optimization - Current Trends

100%

Arabas J.

Journal of Telecommunications and Information Technology

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2011

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tom nr 4

5-10

EN

This article comments on the development of Evolutionary Computation (EC) in the field of global optimization. A brief overview of EC fundamentals is provided together with the discussion of issues of parameter settings and adaptation, advances in the development of theory, new ideas emerging in the EC field and growing availability of massively parallel machines.

4

Global Optimization with the use optimal covering

100%

Woźniak A.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2007

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tom z. 160

309-316

EN

The paper describes global optimization algorithm based on Stratified Covering. Stratified means the feasible set is divided into M disjoint subsets of equal volume, and in each subset N sampling points are uniformly generated. Covering concerns method of uniform generation of points and means that sampling grid is the set of centers of N balls, which cover in the finest manner the subset. An abridget description of optimal stratified sampling and optimal covering algorithms containing only the essential of the methods is presented. for the purpose of illustrating both the actual working and the potentialities of the method, a set of computational results is presented.

5

DNA sequence analysis

80%

Biedrzycki R.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2007

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tom z. 160

35-42

EN

This paper presents a brief survey of computational approaches to the DNA sequence analysis. The basic biological background is presented. The various types of algorithms for pattern construction and gene finding are presented with special attention paid to the application of global optimization methods.

6

A Short Introduction to Stochastic Optimization

80%

Ombach J.

Schedae Informaticae

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2014

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tom Vol. 23

9--20

EN

We present some typical algorithms used for finding global minimum/ maximum of a function defined on a compact finite dimensional set, discuss commonly observed procedures for assessing and comparing the algorithms’ performance and quote theoretical results on convergence of a broad class of stochastic algorithms.

7

Gold rush optimizer : a new population-based metaheuristic algorithm

80%

Zolfi K.

Operations Research and Decisions

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2023

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tom 33

|

nr 1

EN

Today’s world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the gold rush optimizer (GRO), a population-based metaheuristic algorithm that simulates how gold-seekers prospected for gold during the Gold Rush Era using three key concepts of gold prospecting: migration, collaboration, and panning. The GRO algorithm is compared to twelve well-known metaheuristic algorithms on 29 benchmark test cases to assess the proposed approach’s performance. For scientific evaluation, the Friedman and Wilcoxon signed-rank tests are used. In addition to these test cases, the GRO algorithm is evaluated using three real-world engineering problems. The results indicated that the proposed algorithm was more capable than other algorithms in proposing qualitative and competitive solutions.

8

On probabilistic bounds inspired by interval arithmetic

80%

Zilinskas A. , Zilinskas J.

Control and Cybernetics

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2010

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tom Vol. 39, no 2

507-525

EN

A randomized method aimed at evaluation of probabilistic bounds for function values is considered. Stochastic intervals tightly covering ranges of function values with probability close to one are modelled by a randomized method inspired by interval arithmetic. Statistical properties of the modelled intervals are investigated experimentally. The experimental results are discussed with respect to application of this method in the construction of a branch and bound type randomized algorithm for global optimization.

9

Agent-based optimization of advisory strategy parameters

80%

Polnik M. , Kumięga M. , Byrski A.

Journal of Telecommunications and Information Technology

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2013

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tom nr 2

49--55

EN

In this paper, an application of Evolutionary Multiagent Systems (EMAS) and its memetic version to the optimization of advisory strategy (in this case, Sudoku advisory strategy) is considered. The problem is tackled using an EMAS, which has already proven as a versatile optimization technique. Results obtained using EMAS and Parallel Evolutionary Algorithm (PEA) are compared. After giving an insight to the possibilities of decision support in Sudoku solving, an exemplary strategy is defined. Then EMAS and its memetic versions are discussed, and experimental results concerning comparison of EMAS and PEA presented.

10

Non-linear optimization methods for small earthquake locations

80%

Pszczoła G. , Leśniak A.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2004

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tom Vol. 8, No 4

583-590

EN

The problem of locating mine tremors using P-wave arrival times is revisited in the paper. A multidimensional, global, non-linear, constrained optimization method is used as a minimization algorithm for tremor location.In order to see the general properties of the minimized function a few images showing its basins of attractions have been constructed. These pictures enable us to choose efficient algorithms needed to solve location problems. The classical genetic algorithm, pure random search and the most efficient multistart algorithm have been tested. Local minimization methods should be introduced to the location procedure to increase the efficiency of tremor location.

11

Differential evolution with competitive setting of control parameters

80%

Tvrdik J.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2007

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tom Vol. 11, No 1-2

169-179

EN

This paper is focused on the adaptation of control parameters in differential evolution. Competition of various control parameter settings was proposed in order to ensure self-adaptation of parameter values in the search process. Several variants of such algorithm were tested on six functions at four levels of the search-space dimension. The competitive variants of differential evolution have proved to be more reliable and less time-consuming than the standard differential evolution. The competitive variants have also outperformed other tested algorithms in their reliability and convergence rate.

12

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

80%

Zaborski M. , Okulewicz M. , Mańdziuk J.

Foundations of Computing and Decision Sciences

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2020

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tom 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.

13

Interval inversion of well-logging data for objective determination of textural parameters

80%

Dobróka M. , Szabó N. P.

Acta Geophysica

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2011

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tom Vol. 59, no. 5

907-934

EN

A joint inversion method for the evaluation of well-logging data is presented, which is applicable to determine textural parameters, i.e., cementation exponent, saturation exponent and tortuosity factor, simultaneously with conventional petrophysical properties. The inversion techniques used today perform local interpretation. Since the number of unknowns is slightly lower than that of the data estimated locally to one depth-point, a set of marginally overdetermined inverse problems has to be solved. For preserving the overdetermination, textural parameters must be kept constant for longer depth intervals (i.e., 200-300 m), despite the fact that they seem to be varying faster with depth according to field experiences. An inversion method was developed, which inverts data of a greater depth interval jointly in a highly overdetermined inversion procedure and gives a better resolution (10 m or less) estimate for the textural parameters. In the paper, a set of inversion tests on synthetic data as well as a field example prove the feasibility of the method.

14

Fitting reactive force fields using genetic algorithms

80%

Larsson H. R. , Hartke B.

Computer Methods in Materials Science

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2013

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tom Vol. 13, No. 1

120--126

EN

With reactive force fields it is possible to perform atomistic simulations that join the accuracy of quantum chemical treatments (including bond breaking and formation) with the ability to treat hundreds of thousands of atoms on time scales well into the nanosecond regime. To utilize this power in everyday applications requires (I) the assembly of a suitable reference data set of sufficient quality, and (II) a reliable fit of the huge and complex parameter set of a general reactive force field to these reference data. In this contribution, we show that genetic algorithms can be used to achieve goal (II). We discuss algorithm design and implementation aspects (including parallelization) and present an application to azobenzene as real-life example.

PL

Przy pomocy pól sił reakcji możliwe jest przeprowadzenie symulacji atomowych, które łączą w sobie dokładność procesów chemii kwantowej (włączając zrywanie i tworzenie wiązań) oraz zdolność przetwarzania setek tysięcy atomów na skali czasowej w reżim nanosekundowy. Aby wykorzystać takie możliwości w powszechnych aplikacjach wymagane jest (I) zgromadzenie odpowiednich danych referencyjnych zapewniających niezawodną jakość, oraz (II) dopasowanie dużego i złożonego zestawu parametrów pola sił reakcji do tychże danych referencyjnych. W niniejszej pracy wykazujemy, że algorytmy genetyczne mogą być wykorzystane do osiągnięcia celu (II). Omówiony został wstępny algorytm oraz aspekty jego implementacji (w tym zrównoleglenie). Jako rzeczywisty przykład wykorzystania algorytmu przedstawiono jego zastosowanie w azobenzenie.

15

Phenotypic evolution with a mutation based on symmetric α-stable distributions

80%

Obuchowicz A. , Prętki P.

International Journal of Applied Mathematics and Computer Science

|

2004

|

tom 14

|

nr 3

289-316

EN

Multidimensional Symmetric α-Stable (SαS) mutations are applied to phenotypic evolutionary algorithms. Such mutations are characterized by non-spherical symmetry for α<2 and the fact that the most probable distance of mutated points is not in a close neighborhood of the origin, but at a certain distance from it. It is the so-called surrounding effect (Obuchowicz, 2001b; 2003b). For α=2, the SαS mutation reduces to the Gaussian one, and in the case of α=1, the Cauchy mutation is obtained. The exploration and exploitation abilities of evolutionary algorithms, using SαS mutations for different α, are analyzed by a set of simulation experiments. The obtained results prove the important influence of the surrounding effect of symmetric α-stable mutations on both the abilities considered.

16

Gold rush optimizer : a new population-based metaheuristic algorithm

80%

Zolfi K.

Operations Research and Decisions

|

2023

|

tom Vol. 33, No. 1

113--150

EN

Today’s world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the gold rush optimizer (GRO), a population-based metaheuristic algorithm that simulates how gold-seekers prospected for gold during the Gold Rush Era using three key concepts of gold prospecting: migration, collaboration, and panning. The GRO algorithm is compared to twelve well-known metaheuristic algorithms on 29 benchmark test cases to assess the proposed approach’s performance. For scientific evaluation, the Friedman and Wilcoxon signed-rank tests are used. In addition to these test cases, the GRO algorithm is evaluated using three real-world engineering problems. The results indicated that the proposed algorithm was more capable than other algorithms in proposing qualitative and competitive solutions.

17

Parallel global optimization for optimal flood control

80%

Niewiadomska-Szynkiewicz E.

Acta Geophysica Polonica

|

1999

|

tom Vol. 47, nr 1

93-109

EN

The paper considers heuristics optimization methods. The main objective is to present advantages of developing the parallel global algorithms, designed to search for the global minimum (maximum) of performance function. These algorithms are applied to solve complex control problems. A practical example of flood control in a multiple-reservoir water system is analyzed. A two-level control structure with periodic coordination is proposed. The optimization problems consists in determining for all reservoirs. Two heuristic optimization algorithms were applied to solve this problem. The paper describes how the optimization algorithms were chosen and how the parallel implementation improved their efficiency. Results of optimizations and simulations of an on-line reservoir management in the case study of the Upper Vistula river-basin in the southern part of Poland are presented and discussed.

18

(µ + λ) evolution strategy with socio-cognitive mutation

80%

Urbanczyk A. , Kucaba K. , Wojtulewicz M. , Kisiel-Dorohinicki M. , Rutkowski L. , Duda P. , Kacprzyk J. , Yao X. , Chong S. Y. , Byrski A.

Journal of Automation Mobile Robotics and Intelligent Systems

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2024

|

tom Vol. 18, No. 1

1--11

EN

Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

19

Neural network approach to design of distributed hard real-time systems

80%

Martyna J.

Archiwum Informatyki Teoretycznej i Stosowanej

|

1998

|

tom T. 10, z. 3-4

137-151

EN

This paper examines the utility of neural networks for optimization problems occuring in the design of distributed hard real-time systems. In other words, it describes how neural networks may also be used to solve some combinatorial optimization problems, such as: computer locations in distributed system, minimization of overall costs, maximization of system reliability and availability, etc. All requested parameters and constraints in this optimization process fullfil the conditions for design of distributed hard real-time systems. We show that the neural network approach is useful to obtain the good results in the optimization process. Numerical experimentation confirms the appropriateness of this approach.

PL

Podstawowym problemem dotyczącym projektowania rozproszonych systemów komputerowych nieprzekraczalnego czasu krytycznego jest opracowanie jego architektury sprzętowej. Polega ono m.in. na przydziale komputerów o określonych szybkościach przetwarzania do poszczególnych węzłów przyjętej topologii rozproszonego systemu. W pracy zaproponowano użycie sztucznej sieci neuronowej do projektowania rozproszonych systemów komputerowych nieprzekraczalnego czasu krytycznego. Dzieki jej zastosowaniu zminimalizowano koszty inwestycyjne i operacyjne konstrukcji takiego systemu, jak również przeprowadzono maksymalizację jego niezawodności i osiągalności.

20

A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization

80%

Styrcz A. , Mrozek J. , Mazur G.

International Journal of Applied Mathematics and Computer Science

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2011

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tom 21

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nr 3

559-566

EN

A novel, neural network controlled, dynamic evolutionary algorithm is proposed for the purposes of molecular geometry optimization. The approach is tested for selected model molecules and some molecular systems of importance in biochemistry. The new algorithm is shown to compare favorably with the standard, statically parametrized memetic algorithm.