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Genetic algorithm and particle swarm optimization combined with powell method

Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, R.
Fonte: AIP Conference Proceedings Publicador: AIP Conference Proceedings
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
66.04%
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin’s Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

Jointly multi-user detection and channel estimation with genetic algorithm

Neto, Fernando Ciriaco Dias; ABRAO, Taufik; Toledo, Antonio Fischer de; Jeszensky, Paul Jean Etienne
Fonte: WILEY-BLACKWELL Publicador: WILEY-BLACKWELL
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.06%
This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multiuser channel estimation (MuChE) and detection problems at its maximum-likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi-user detection (MuD) show that the proposed genetic algorithm multi-user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi-user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near-optimum multi-user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi-user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence...

Grid job scheduling using Route with Genetic Algorithm support

MELLO, Rodrigo Fernandes de; ANDRADE FILHO, Jose A.; SENGER, Luciano J.; YANG, Laurence T.
Fonte: SPRINGER Publicador: SPRINGER
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.05%
In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route...

Proposição automática de reforços em redes de distribuição de energia elétrica utilizando programação linear e algoritmo genético.; Automatic proposal of reinforcements in power distribution networks using linear programming and genetic algorithm.

Su, Pei Fei
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 11/08/2006 PT
Relevância na Pesquisa
66.05%
Este trabalho tem por objetivo apresentar uma metodologia para localização e proposição de reforços no sistema de distribuição de energia elétrica através de programação linear, PL, e algoritmo genético, AG. A técnica de PL utilizada para a localização de pontos de reforços e, principalmente, novas subestações de distribuição, é baseada no algoritmo de ?out-of-kilter?, um conhecido algoritmo de transporte. A seleção de melhores alternativas é solucionada através do AG, que permite a modelagem de redes com proporções reais e possibilita a obtenção de resultados em tempos de execução compatíveis para aplicação de atividades em planejamento de sistemas de distribuição de energia. O modelo de algoritmo proposto aloca automaticamente novos reforços, como o recondutoramento de trechos da rede e a expansão de subestações existentes, complementando os reforços candidatos, novas subestações e novos alimentadores, propostos previamente pelo modelo de PL. A metodologia proposta é aplicada à resolução de uma rede de distribuição real, possibilitando a análise da potencialidade que esta modelagem pode oferecer.; This dissertation presents a methodology for the allocation and proposal of new reinforcements in electric distribution systems through linear programming (LP) and genetic algorithm (GA). The linear programming technique used for the allocation of new reinforcements...

Algoritmo genético e espectroscopia no infravermelho - algumas aplicações na indústria cosmética; Genetic algorithm and infrared spectroscopy - some applications in the cosmetic industry

Amendola, Marcos Coelho
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 29/01/2007 PT
Relevância na Pesquisa
66.08%
Este trabalho discute o desenvolvimento de um algoritmo genético escrito em linguagem VBA para Excel e suas aplicações. O algoritmo elaborado foi utilizado em combinação com a técnica de FTIR-ATR para o desenvolvimento de metodologias aplicáveis na indústria cosmética e de saneantes, tais como a quantificação de surfactantes e bactericidas. Algumas modificações introduzidas no algoritmo foram estudadas através das aplicações selecionadas, destacando-se a introdução de técnicas de paralelismo que possibilitam a quantificação de mais de um analito em uma mesma execução do algoritmo. Este tipo de técnica foi aplicado na quantificação o-benzil p-cloro fenol, o-fenil fenol e etanol, em uma mistura dos três componentes, utilizada como matéria prima (bactericida) na indústria de saneantes. Também com o uso do algoritmo, FTIR-ATR e calibração linear múltipla, foram desenvolvidos métodos para determinação do ingrediente ativo total da mistura lauril sulfato de amônio/lauril éter sulfato de sódio em amostras de shampoo. Comparados aos métodos usuais de titulação, cromatografia líquida ou gasosa, os novos métodos se distinguem por não requererem preparo algum da mostra, nem consumirem solventes orgânicos...

Análise inversa de estruturas com utilização de algoritmos genéticos.; Inverse analysis of structures with genetic algorithm management.

Leite, Francisco Augusto Pereira
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 30/11/2006 PT
Relevância na Pesquisa
66.03%
O Homem tem desde o passado, tentado controlar a natureza. Um dos meios utilizados para isto, é sua observação do mundo. Através desta observação, tenta entender os fenômenos da natureza para fazer teorias e modelos. Charles Darwin, em seu trabalho Teoria da Evolução das Espécies, nos dá informações para o conhecimento de uma das mais importantes leis da natureza : sobrevive para a próxima geração o individuo mais forte. O Algoritmo Genético, pesquisado neste trabalho, é o exemplo disso. John Holland fez um Algoritmo Genético baseado na teoria de Darwin, que procura pelas melhores soluções para resolver um problema específico. Nada mais do que a simulação da teoria de Darwin. Nós pretendemos neste trabalho, estudar o Algoritmo Genético de Holland e através dele, analisar uma estrutura para encontrar seus parâmetros elásticos.; The men has since the past, tryed to control the nature. One of the way utilized for this, is his observation of the world. Through his observation, tries to understand the nature's fenomena, to making theories and models. Charles Darwin, in his work Theories of Species Evolution, gives us informations for the knowledges of one of the most important nature's laws: survives to the next generation the strongest individual . The Genetic Algorithm...

Transmission system expansion planning by an extended genetic algorithm

Gallego, R. A.; Monticelli, A.; Romero, R.
Fonte: Institute of Electrical and Electronics Engineers (IEEE) Publicador: Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Artigo de Revista Científica Formato: 329-335
ENG
Relevância na Pesquisa
66.06%
The paper presents an extended genetic algorithm for solving the optimal transmission network expansion planning problem. Two main improvements have been introduced in the genetic algorithm: (a) initial population obtained by conventional optimisation based methods; (b) mutation approach inspired in the simulated annealing technique, the proposed method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Excellent performance is reported in the test results section of the paper for a difficult large-scale real-life problem: a substantial reduction in investment costs has been obtained with regard to previous solutions obtained via conventional optimisation methods and simulated annealing algorithms; statistical comparison procedures have been employed in benchmarking different versions of the genetic algorithm and simulated annealing methods.

Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem

Escobar Z, Antonio H.; Gallego R, Ramon A.; Romero L, Ruben A.
Fonte: Univ Nac Colombia, Fac Ingenieria Publicador: Univ Nac Colombia, Fac Ingenieria
Tipo: Artigo de Revista Científica Formato: 127-143
ENG
Relevância na Pesquisa
66.06%
This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found in less time when applied to low complexity electricity distribution networks and better quality solutions for highly complex networks when compared to a genetic algorithm using random initial populations.

A genetic algorithm for crop rotation

Filho, Angelo Aliano; De Oliveira Florentino, Helenice; Pato, Margarida Vaz
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 454-457
ENG
Relevância na Pesquisa
66.06%
In the last few years, crop rotation has gained attention due to its economic, environmental and social importance which explains why it can be highly beneficial for farmers. This paper presents a mathematical model for the Crop Rotation Problem (CRP) that was adapted from literature for this highly complex combinatorial problem. The CRP is devised to find a vegetable planting program that takes into account green fertilization restrictions, the set-aside period, planting restrictions for neighboring lots and for crop sequencing, demand constraints, while, at the same time, maximizing the profitability of the planted area. The main aim of this study is to develop a genetic algorithm and test it in a real context. The genetic algorithm involves a constructive heuristic to build the initial population and the operators of crossover, mutation, migration and elitism. The computational experiment was performed for a medium dimension real planting area with 16 lots, considering 29 crops of 10 different botanical families and a two-year planting rotation. Results showed that the algorithm determined feasible solutions in a reasonable computational time, thus proving its efficacy for dealing with this practical application.

Stacking sequence optimisation of composite panels subjected to slamming impact loads using a genetic algorithm

Khedmati,Mohammad Reza; Sangtabi,Mohammad Rezai; Fakoori,Mehdi
Fonte: Associação Brasileira de Ciências Mecânicas Publicador: Associação Brasileira de Ciências Mecânicas
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2013 EN
Relevância na Pesquisa
66.06%
Optimisation of stacking sequence for composite panels under slamming impact loads using a genetic algorithm method is studied in this paper. For this purpose, slamming load is assumed to have a uniform distribution with a triangular-pulse type of intensity function. In order to perform optimisation based on a genetic algorithm, a special code is written in MATLAB software environment. The optimiser is coupled with the commercial software ANSYS in order to analyse the composite panel under study and calculate the central deflection. After validation, different cases of stacking sequence optimisation are investigated for a variety of composite panels. The investigations include symmetric as well as asymmetric conditions of stacking sequence. Results obtained from these analyses reveal the fact that the adopted approach based on a genetic algorithm is highly capable of performing such optimisations.

The Compact Memetic Algorithm

Merz, Peter
Fonte: Universidade de Tubinga Publicador: Universidade de Tubinga
Tipo: Teil einer Konferenzveröffentlichung
EN
Relevância na Pesquisa
66.04%
Optimization by probabilistic modeling is a growing research field in evolutionary computation. An example is the compact genetic algorithm (cGA), in which the population of a genetic algorithm (GA) is represented as a probability distribution over the set of solutions. Both cGA algorithm and the order-one behavior of a simple GA with uniform crossover are operationally equivalent. The cGA is much easier to implement and requires less memory. In this paper, memetic algorithms (MAs) are investigated in which the population is replaced by a probability vector analogously to the cGA. The resulting compact memetic algorithms (cMAs) hence require less memory, are easier to implement and require fewer parameters than other MAs. It is shown that cMAs with and without additional recombination perform comparable to or better than population-based MAs on a set of benchmark instances of the unconstrained binary quadratic programming problem.

Optimization of a genetic algorithm for searching molecular conformer space

Brain, Zoe E.; Addicoat, Matthew A.
Fonte: American Institute of Physics (AIP) Publicador: American Institute of Physics (AIP)
Tipo: Artigo de Revista Científica Formato: 10 pages
Relevância na Pesquisa
66.09%
We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA-- a meta-genetic algorithm--was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm.

A Modified Genetic Algorithm and Switch-Based Neural Network Model Applied to Misuse-Based Intrusion Detection

Stewart, IAN
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 1260857 bytes; application/pdf
EN; EN
Relevância na Pesquisa
66.05%
As our reliance on the Internet continues to grow, the need for secure, reliable networks also increases. Using a modified genetic algorithm and a switch-based neural network model, this thesis outlines the creation of a powerful intrusion detection system (IDS) capable of detecting network attacks. The new genetic algorithm is tested against traditional and other modified genetic algorithms using common benchmark functions, and is found to produce better results in less time, and with less human interaction. The IDS is tested using the standard benchmark data collection for intrusion detection: the DARPA 98 KDD99 set. Results are found to be comparable to those achieved using ant colony optimization, and superior to those obtained with support vector machines and other genetic algorithms.; Thesis (Master, Computing) -- Queen's University, 2009-03-03 13:28:23.787

"Extração de conhecimento de redes neurais artificiais utilizando sistemas de aprendizado simbólico e algoritmos genéticos" ; Extraction of knowledge from Artificial Neural Networks using Symbolic Machine Learning Systems and Genetic Algorithm

Milaré, Claudia Regina
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 24/06/2003 PT
Relevância na Pesquisa
66.04%
Em Aprendizado de Máquina - AM não existe um único algoritmo que é sempre melhor para todos os domínios de aplicação. Na prática, diversas pesquisas mostram que Redes Neurais Artificiais - RNAs têm um 'bias' indutivo apropriado para diversos domínios. Em razão disso, RNAs têm sido aplicadas na resolução de vários problemas com desempenho satisfatório. Sistemas de AM simbólico possuem um 'bias' indutivo menos flexível do que as RNAs. Enquanto que as RNAs são capazes de aprender qualquer função, sistemas de AM simbólico geralmente aprendem conceitos que podem ser descritos na forma de hiperplanos. Por outro lado, sistemas de AM simbólico representam o conceito induzido por meio de estruturas simbólicas, as quais são geralmente compreensíveis pelos seres humanos. Assim, sistemas de AM simbólico são preferíveis quando é essencial a compreensibilidade do conceito induzido. RNAs carecem da capacidade de explicar suas decisões, uma vez que o conhecimento é codificado na forma de valores de seus pesos e 'thresholds'. Essa codificação é difícil de ser interpretada por seres humanos. Em diversos domínios de aplicação, tal como aprovação de crédito e diagnóstico médico, prover uma explicação sobre a classificação dada a um determinado caso é de crucial importância. De um modo similar...

Project scheduling under multiple resources constraints using a genetic algorithm

Magalhães-Mendes, J.
Fonte: World Scientific and Engineering Academy and Society (WSEAS) Publicador: World Scientific and Engineering Academy and Society (WSEAS)
Tipo: Artigo de Revista Científica
Publicado em //2008 ENG
Relevância na Pesquisa
66.04%
The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

A genetic-algorithm based decoder for low density parity check codes

Scandurra,A. G.; Dai Pra,A. L.; Arnone,L.; Passoni,L.; Castineira Moreira,J.
Fonte: Latin American applied research Publicador: Latin American applied research
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/07/2006 EN
Relevância na Pesquisa
66.1%
This paper presents a Genetic-Algorithm based decoder for a medium-sized Low Density Parity Check code (GAMD decoder). The main advantage of the proposed GAMD decoder is that no information on the noise level transmission channel is required, an essential condition for the well-known sum product algorithm. The proposed methodology combines a Genetic Algorithm stage with a meta-decision process. Genetic Algorithms were selected due to their capacity to solve this type of multiple minimum. Encouraging results were reached when comparing the Bit Error Rate (BER) performance of the proposed algorithm with that of the traditional sum-product decoding algorithm. The performance of the proposed decoder is very close to that of the optimal sum-product decoder, with the additional benefit of not requiring channel information (signal-to-noise ratio). In order to improve Bit Error Rate performance and/or reduce the complexity of the proposed decoder, the fitness function and parameters of the GA can be optimized.

FINDING FUZZY IDENTIFICATION SYSTEM PARAMETERS USING A NEW DYNAMIC MIGRATION PERIOD-BASED DISTRIBUTED GENETIC ALGORITHM

CASTRO,MARCO ANTONIO; HERRERA,FRANCISCO
Fonte: DYNA Publicador: DYNA
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2009 EN
Relevância na Pesquisa
66.04%
This paper presents a distributed genetic algorithm with dynamic determination of the migration period. The algorithm is especially well suited for the on line estimation of a fuzzy identification system parameters, using heterogeneous clusters. The results of the optimization of a TSK (Takagi-Sugeno-Kang) system for the identification of a biotechnological (fermentative) process including the solution’s quality and speedup analysis are presented. Comparative results using static and dynamic migration periods on the genetic algorithm are also presented.

Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming

Belloufi,A.; Assas,M.; Rezgui,I.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/02/2013 EN
Relevância na Pesquisa
66.08%
The determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for the optimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizing the production cost under a set of machining constraints. The genetic algorithm (GA) is the main optimizer of this algorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergence characteristics and robustness of the proposed method have been explored through comparisons with results reported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequential quadratic programming is effective compared to other techniques carried out by different researchers.

The use of a genetic algorithm in optical thin film design and optimisation

Ejigu,Efrem K.; Lacquet,Beartys M.
Fonte: South African Journal of Science Publicador: South African Journal of Science
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/08/2010 EN
Relevância na Pesquisa
66.13%
We used a genetic algorithm in the design and optimisation of optical thin films and present the effects of the choice of variables, refractive index and optical thickness, in both applications of this algorithm, in this paper. The Fourier transform optical thin film design method was used to create a starting population, which was later optimised by the genetic algorithm. In the genetic algorithm design application, the effect of the choice of variable was not distinct, as it depended on the type of design specification. In the genetic algorithm optimisation application, the choice of refractive index as a variable showed a better performance than that of optical thickness. The results of this study indicate that a genetic algorithm is more effective in the design application than in the optimisation application of optical thin film synthesis.

Slope modification of open pit wall using a genetic algorithm - case study: southern wall of the 6th Golbini Jajarm bauxite mine

Goshtasbi,K.; Ataei,M.; Kalatehjary,R.
Fonte: Journal of the Southern African Institute of Mining and Metallurgy Publicador: Journal of the Southern African Institute of Mining and Metallurgy
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/10/2008 EN
Relevância na Pesquisa
66.06%
In this paper a genetic algorithm is used in a heavily jointed rock mass in order to investigate the critical circular slip surface and modification of slope surface. This method was applied to the southern wall of the 6th Golbini Jajarm bauxite mine. The mine is the largest bauxite deposit in Iran, located to the northeast of the town of Jajarm in the Khorasan province. Estimated reserve of bauxite in this deposit is about 160 million tonnes. Field and laboratory investigations were conducted in order to determine rock mass behaviour. A genetic algorithm code that uses the Simplified Bishop method as an objective function was developed for finding the safety factor of circular slip surfaces. Sensitivity analysis was applied to determine the optimum values of the genetic algorithm variables, such as population size, selection method, crossover and mutation rates. After finding the critical circular slip surface, slope modification is carried out by removing unstable sections from marked critical slip surfaces, and this process is repeated until the last unsafe section is removed. Based on this code, modification occurred during 7 steps, by reaching a safety factor of 1.3 in the last step. Finally, the modified slope angle of the southern wall of the 6th Golbini Jajarm bauxite mine was determined to be 48.44 degrees.