Academic literature on the topic 'Hybrid Evolution Algorithms'
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Journal articles on the topic "Hybrid Evolution Algorithms"
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Ahandani, Morteza Alinia, and Hosein Alavi-Rad. "Hybridizing Shuffled Frog Leaping and Shuffled Complex Evolution Algorithms Using Local Search Methods." International Journal of Applied Evolutionary Computation 5, no. 1 (January 2014): 30–51. http://dx.doi.org/10.4018/ijaec.2014010103.
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In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two meta-heuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hybrid strategy that is combined from evolutionary process of each subset in related algorithm and a local search method. These hybrid algorithms are evaluated on low and high dimensional continuous benchmark functions and the obtained results are compared with their non-hybrid competitors. The obtained results demonstrate that the hybrid algorithm combined from shuffled frog leaping and Nelder–Mead simplex has a better success rate but a higher number of function evaluations on low-dimensional functions than the shuffled frog leaping. Whereas on high-dimensional functions it has a better success rate and a faster performance. Also the hybrid algorithm combined from shuffled complex evolution and bidirectional random optimization obtains a better performance in terms of success rate and function evaluations than shuffled complex evolution on low dimensional functions; whereas on high-dimensional functions, it obtains a better success rate but a slower performance. Also a comparison of our hybrid algorithms with the other evolutionary algorithms reported in the literature confirms our proposed algorithms have the best performance among all compared algorithms.
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Kaelo, P., and M. M. Ali. "Differential evolution algorithms using hybrid mutation." Computational Optimization and Applications 37, no. 2 (March 6, 2007): 231–46. http://dx.doi.org/10.1007/s10589-007-9014-3.
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Kumar, N. Suresh, and Pothina Praveena. "Evolution of hybrid distance based kNN classification." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 510. http://dx.doi.org/10.11591/ijai.v10.i2.pp510-518.
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<span id="docs-internal-guid-b63d466d-7fff-f94f-7540-9cb92d7bb505"><span>The evolution of classification of opinion mining and user review analysis span from decades reaching into ubiquitous computing in efforts such as movie review analysis. The performance of linear and non-linear models are discussed to classify the positive and negative reviews of movie data sets. The effectiveness of linear and non-linear algorithms are tested and compared in-terms of average accuracy. The performance of various algorithms is tested by implementing them on internet movie data base (IMDB). The hybrid kNN model optimizes the performance classification interns of accuracy. The accuracy of polarity prediction rate is improved with random-distance-weighted-kNN-ABC when compared with kNN algorithm applied alone.</span></span>
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Krishna, R. V. V., and S. Srinivas Kumar. "Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation." Engineering, Technology & Applied Science Research 6, no. 5 (October 23, 2016): 1182–86. http://dx.doi.org/10.48084/etasr.799.
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This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD) which is a modification of Weber Local Descriptor (WLD) is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.
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Abi, Soufiane, and Bachir Benhala. "An optimal design of current conveyors using a hybrid-based metaheuristic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6653. http://dx.doi.org/10.11591/ijece.v12i6.pp6653-6663.
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<span lang="EN-US">This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.</span>
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Kang, Yan, Zhong Min Wang, Ying Lin, and Xiang Yun Guo. "A Hybrid Differential Evolution Scheduling Algorithm to Heterogeneous Distributed System." Applied Mechanics and Materials 631-632 (September 2014): 271–75. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.271.
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This paper presents a differential evolution algorithm with designed greedy heuristic strategy to solve the task scheduling problem. The static task scheduling problem is NP-complete and is a critic issue in parallel and distributed computing environment. A vector consists of a task permutation assigned to each individual in the target population by using DE mutation and crossover operators. A heuristic strategy is used to generate the feasible solutions as there a lot of infeasible solutions in the solution space as the size of the problem increase. And the strategies of the particle swarm algorithm are employed to modify the DE crossover operator for speeding up the search to optimal solution. And then, the individual is replaced with the corresponding target individual if it is global best or local best in terms of fitness. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms. The performances of the proposed algorithms are tested on the benchmark and compared to the best-known solutions available. The computational results demonstrate that effectively and efficiency of the presented algorithm.
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Abdel-Basset, Mohamed, Reda Mohamed, Waleed Abd Abd Elkhalik, Marwa Sharawi, and Karam M. Sallam. "Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution." Mathematics 10, no. 21 (October 31, 2022): 4049. http://dx.doi.org/10.3390/math10214049.
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Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched.
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Ghosh, Tarun Kumar, and Sanjoy Das. "A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid." International Journal of Distributed Systems and Technologies 9, no. 2 (April 2018): 1–15. http://dx.doi.org/10.4018/ijdst.2018040101.
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Scheduling jobs in computational Grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.
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Ibrahim, Abdelmonem M., and Mohamed A. Tawhid. "A hybridization of differential evolution and monarch butterfly optimization for solving systems of nonlinear equations." Journal of Computational Design and Engineering 6, no. 3 (October 25, 2018): 354–67. http://dx.doi.org/10.1016/j.jcde.2018.10.006.
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Abstract In this study, we propose a new hybrid algorithm consisting of two meta-heuristic algorithms; Differential Evolution (DE) and the Monarch Butterfly Optimization (MBO). This hybrid is called DEMBO. Both of the meta-heuristic algorithms are typically used to solve nonlinear systems and unconstrained optimization problems. DE is a common metaheuristic algorithm that searches large areas of candidate space. Unfortunately, it often requires more significant numbers of function evaluations to get the optimal solution. As for MBO, it is known for its time-consuming fitness functions, but it traps at the local minima. In order to overcome all of these disadvantages, we combine the DE with MBO and propose DEMBO which can obtain the optimal solutions for the majority of nonlinear systems as well as unconstrained optimization problems. We apply our proposed algorithm, DEMBO, on nine different, unconstrained optimization problems and eight well-known nonlinear systems. Our results, when compared with other existing algorithms in the literature, demonstrate that DEMBO gives the best results for the majority of the nonlinear systems and unconstrained optimization problems. As such, the experimental results demonstrate the efficiency of our hybrid algorithm in comparison to the known algorithms. Highlights This paper proposes a new hybridization of differential evolution and monarch butterfly optimization. Solve system of nonlinear equations and unconstrained optimization problem. The efficiency and effectiveness of our algorithm are provided. Experimental results prove the superiority of our algorithm over the state-of-the-arts.
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Brévilliers, Mathieu, Julien Lepagnot, Lhassane Idoumghar, Maher Rebai, and Julien Kritter. "Hybrid differential evolution algorithms for the optimal camera placement problem." Journal of Systems and Information Technology 20, no. 4 (November 12, 2018): 446–67. http://dx.doi.org/10.1108/jsit-09-2017-0081.
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PurposeThis paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.Design/methodology/approachThis problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.FindingsThe experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice.Originality/valueUp to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations to fully benefit from the DE mutation scheme.
Dissertations / Theses on the topic "Hybrid Evolution Algorithms"
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Kafafy, Ahmed. "Hybrid Evolutionary Metaheuristics for Multiobjective Decision Support." Thesis, Lyon 1, 2013. http://www.theses.fr/2013LYO10184/document.
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La prise de décision est une partie intégrante de notre vie quotidienne où le décideur est confronté à des problèmes composés de plusieurs objectifs habituellement contradictoires. Dans ce travail, nous traitons des problèmes d'optimisation multiobjectif dans des espaces de recherche continus ou discrets. Nous avons développé plusieurs nouveaux algorithmes basés sur les métaheuristiques hybrides évolutionnaires, en particulier sur l'algorithme MOEA/D. Nous avons proposé l'algorithme HEMH qui utilise l'algorithme DM-GRASP pour construire une population initiale de solutions de bonne qualité dispersées le long de l'ensemble des solutions Pareto optimales. Les résultats expérimentaux montrent la supériorité de toutes les variantes hybrides proposées sur les algorithmes originaux MOEA/D et SPEA2. Malgré ces bons résultats, notre approche possède quelques limitations, levées dans une version améliorée de HEMH : HEMH2 et deux autres variantes HEMHde et HEMHpr. Le Adaptive Binary DE inclus dans les HEMH2 et HEMHde a de meilleures capacités d'exploration qui pallient aux capacités de recherche locale contenues dans la HEMH, HEMH2 et HEMHde. Motivés par ces résultats, nous avons proposé un nouvel algorithme baptisé HESSA pour explorer un espace continu de recherche où le processus de recherche est réalisé par différentes stratégies de recherche. Les résultats expérimentaux montrent la supériorité de HESSA à la fois sur MOEA/D et dMOPSO. Tous les algorithmes proposés ont été vérifiés, testé et comparés à certaines méthodes MOEAs. Les résultats expérimentaux montrent que toutes les propositions sont très compétitives et peuvent être considérés comme une alternative fiableMany real-world decision making problems consist of several conflicting objectives, the solutions of which is called the Pareto-optimal set. Hybrid metaheuristics proved their efficiency in solving these problems. They tend to enhance search capabilities by incorporating different metaheuristics. Thus, we are concerned with developing new hybrid schemes by incorporating different strategies with exploiting the pros and avoiding the drawback of the original ones. First, HEMH is proposed in which the search process includes two phases DMGRASP obtains an initial set of efficient solutions in the 1st phase. Then, greedy randomized path-relinking with local search or reproduction operators explore the non-visited regions. The efficient solutions explored over the search are collected. Second, a comparative study is developed to study the hybridization of different metaheuristics with MOEA/D. The 1st proposal combines adaptive discrete differential Evolution with MOEA/D. The 2nd combines greedy path-relinking with MOEA/D. The 3rd and the 4th proposals combine both of them in MOEA/D. Third, an improved version of HEMH is presented. HEMH2 uses inverse greedy to build its initial population. Then, differential evolution and path-relink improves these solutions by investigating the non-visited regions in the search space. Also, Pareto adaptive epsilon concept controls the archiving process. Motivated by the obtained results, HESSA is proposed to solve continuous problems. It adopts a pool of search strategies, each of which has a specified success ratio. A new offspring is generated using a randomly selected one. Then, the success ratios are adapted according to the success of the generated offspring. The efficient solutions are collected to act as global guides. The proposed algorithms are verified against the state of the art MOEAs using a set of instances from literature. Results indicate that all proposals are competitive and represent viable alternatives
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Naldi, Murilo Coelho. "Agrupamento híbrido de dados utilizando algoritmos genéticos." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07112006-080351/.
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Técnicas de Agrupamento vêm obtendo bons resultados quando utilizados em diversos problemas de análise de dados, como, por exemplo, a análise de dados de expressão gênica. Porém, uma mesma técnica de agrupamento utilizada em um mesmo conjunto de dados pode resultar em diferentes formas de agrupar esses dados, devido aos possíveis agrupamentos iniciais ou à utilização de diferentes valores para seus parâmetros livres. Assim, a obtenção de um bom agrupamento pode ser visto como um processo de otimização. Esse processo procura escolher bons agrupamentos iniciais e encontrar o melhor conjunto de valores para os parâmetros livres. Por serem métodos de busca global, Algoritmos Genéticos podem ser utilizados durante esse processo de otimização. O objetivo desse projeto de pesquisa é investigar a utilização de Técnicas de Agrupamento em conjunto com Algoritmos Genéticos para aprimorar a qualidade dos grupos encontrados por algoritmos de agrupamento, principalmente o k-médias. Esta investigação será realizada utilizando como aplicação a análise de dados de expressão gênica. Essa dissertação de mestrado apresenta uma revisão bibliográfica sobre os temas abordados no projeto, a descrição da metodologia utilizada, seu desenvolvimento e uma análise dos resultados obtidos.Clustering techniques have been obtaining good results when used in several data analysis problems, like, for example, gene expression data analysis. However, the same clustering technique used for the same data set can result in different ways of clustering the data, due to the possible initial clustering or the use of different values for the free parameters. Thus, the obtainment of a good clustering can be seen as an optimization process. This process tries to obtain good clustering by selecting the best values for the free parameters. For being global search methods, Genetic Algorithms have been successfully used during the optimization process. The goal of this research project is to investigate the use of clustering techniques together with Genetic Algorithms to improve the quality of the clusters found by clustering algorithms, mainly the k-means. This investigation was carried out using as application the analysis of gene expression data, a Bioinformatics problem. This dissertation presents a bibliographic review of the issues covered in the project, the description of the methodology followed, its development and an analysis of the results obtained.
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Ghoman, Satyajit Sudhir. "A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23113.
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The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness.The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE.
The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a non-conventional supersonic tailless air vehicle wing shape optimization problem.
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Caetano, Samuel Sabino. "O uso de algoritmos evolutivos para a formação de grupos na aprendizagem colaborativa no contexto corporativo." Universidade Federal de Goiás, 2013. http://repositorio.bc.ufg.br/tede/handle/tede/3195.
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Increasingly, learning in groups has become present in school environments. This fact is also part of the organizations, when considers learning in the workplace. Conscious of the importance of group learning at the workplace (CSCL@Work) emerges as an application area. In Computer Supported Collaborative Learning(CSCL), researchers have been struggling to maximize the performance of groups by techniques for forming groups. Is that why this study developed three (3) algorithmic approaches to formation of intraheterogeneous and inter-homogeneous groups, as well as a model proposed in this work in which integrates dichotomous functional characteristics and preferred roles. We made an algorithm that generates random groups, a Canonical Genetic Algorithm and Hybrid Genetic Algorithm. We obtained the input data of the algorithm by a survey conducted at the Court of the State of Goiás to identify dichotomous functional characteristics, and after we categorize these characteristics, based on the data found and the model proposed group formation. Starting at real data provided of employees whom participated in a course by Distance Education (EaD), we apply the model and we obtained the input data related to functional features. As regards the favorite roles, we assigned randomly values to the employees aforementioned, from a statistical statement made by Belbin into companies in the United Kingdom. Then, we executed the algorithms in three test cases, one considering the preferred papers and functional characteristics, while the other two separately considering each of these perspectives. Based on the results obtained, we found that the hybrid genetic algorithm outperforms the canonical genetic algorithm and random generator.
A aprendizagem em grupos tem se tornado realidade cada vez mais presente nos ambientes de ensino. Esta realidade também faz parte das organizações quando considera-se a aprendizagem no contexto do trabalho. Cientes da importância da aprendizagem em grupo no ambiente de trabalho, uma nova abordagem, denominada CSCL@Work, surge como uma aplicação da área Aprendizagem Colaborativa Apoiada pelo Computador, no inglês, Computer Supported Collaborative Learning (CSCL), no ambiente de trabalho. Em CSCL, pesquisadores tem se esforçado cada vez mais para maximizar o desempenho dos grupos através de técnicas para formação de grupos. Por isso neste trabalho desenvolvemos 3 (três) abordagens algorítmicas para formação de grupos intra-heterogêneos e inter-homogêneos, a partir de um modelo proposto nesta pesquisa, que integra características funcionais dicotômicas e papéis preferidos. Confeccionamos um algoritmo que gera grupos aleatoriamente, um algoritmo genético canônico e um algoritmo genético híbrido. Para obter os dados de entrada do algoritmo, realizamos uma pesquisa no Tribunal de Justiça do Estado de Goiás para identificar características funcionais dicotômicas, categorizamos estas características, com base nos dados encontrados e no modelo de formação de grupos proposto. A partir de dados reais fornecidos de funcionários que participaram de um curso por Educação a Distância (EaD), aplicamos o modelo e obtivemos os dados de entrada relativos às características funcionais. Quanto aos papéis preferidos, atribuímos os valores aleatoriamente aos funcionários mencionados, partindo de um levantamento estatístico feito por Belbin em empresas no Reino Unido. Em seguida, executamos os algoritmos em três casos de testes, um considerando as características funcionais e papéis preferidos, e os outros dois considerando separadamente cada uma destas perspectivas. A partir dos resultados obtidos, constatamos que o algoritmo genético híbrido obtém resultados superiores ao algoritmo genético canônico e método aleatório.
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Inclan, Eric. "The Development of a Hybrid Optimization Algorithm for the Evaluation and Optimization of the Asynchronous Pulse Unit." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1582.
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The effectiveness of an optimization algorithm can be reduced to its ability to navigate an objective function’s topology. Hybrid optimization algorithms combine various optimization algorithms using a single meta-heuristic so that the hybrid algorithm is more robust, computationally efficient, and/or accurate than the individual algorithms it is made of. This thesis proposes a novel meta-heuristic that uses search vectors to select the constituent algorithm that is appropriate for a given objective function. The hybrid is shown to perform competitively against several existing hybrid and non-hybrid optimization algorithms over a set of three hundred test cases. This thesis also proposes a general framework for evaluating the effectiveness of hybrid optimization algorithms. Finally, this thesis presents an improved Method of Characteristics Code with novel boundary conditions, which better characterizes pipelines than previous codes. This code is coupled with the hybrid optimization algorithm in order to optimize the operation of real-world piston pumps.
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Burdelis, Mauricio Alexandre Parente. "Ajuste de taxas de mutação e de cruzamento de algoritmos genéticos utilizando-se inferências nebulosas." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-14082009-180444/.
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Neste trabalho foi realizada uma proposta de utilização de Sistemas de Inferência Nebulosos para controlar, em tempo de execução, parâmetros de Algoritmos Genéticos. Esta utilização busca melhorar o desempenho de Algoritmos Genéticos diminuindo, ao mesmo tempo: a média de iterações necessárias para que um Algoritmo Genético encontre o valor ótimo global procurado; bem como diminuindo o número de execuções do mesmo que não são capazes de encontrar o valor ótimo global procurado, nem mesmo para quantidades elevadas de iterações. Para isso, foram analisados os resultados de diversos experimentos com Algoritmos Genéticos, resolvendo instâncias dos problemas de Minimização de Funções e do Caixeiro Viajante, sob diferentes configurações de parâmetros. Com base nos resultados obtidos a partir destes experimentos, foi proposto um modelo com a troca de valores de parâmetros de Algoritmos Genéticos, em tempo de execução, pela utilização de Sistemas de Inferência Nebulosos, de forma a melhorar o desempenho do sistema, minimizando ambas as medidas citadas anteriormente.This work addressed a proposal of the application of Fuzzy Systems to adjust parameters of Genetic Algorithms, during execution time. This application attempts to improve the performance of Genetic Algorithms by diminishing, at the same time: the average number of necessary generations for a Genetic Algorithm to find the desired global optimum value, as well as diminishing the number of executions of a Genetic Algorithm that are not capable of finding the desired global optimum value even for high numbers of generations. For that purpose, the results of many experiments with Genetic Algorithms were analyzed; addressing instances of the Function Minimization and the Travelling Salesman problems, under different parameter configurations. With the results obtained from these experiments, a model was proposed, for the exchange of parameter values of Genetic Algorithms, in execution time, by using Fuzzy Systems, in order to improve the performance of the system, minimizing both of the measures previously cited.
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Hoc, Tran Duc, and 陳德學·. "Hybrid Multiple Objective Differential Evolution Algorithms for Optimizing Resource Trade-offs of Project Scheduling." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/4qebpn.
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博士國立臺灣科技大學
營建工程系
103
Construction management everywhere faces problems and challenges. Resource scheduling is a crucial part of project planning of any management companies. Successful tradeoff optimization resource scheduling problems within the project scope is necessary to maximize overall company benefits. This study investigated the potential use of various advanced techniques to improve multiple objective Differential Evolution. Three hybrid multiple objective Differential Evolution (MODE) algorithms that integrate chaotic maps, opposition-based learning technique and Artificial Bee Colony are introduced to solve the resource scheduling problems. Firstly, chaotic initialized adaptive multiple objective Differential Evolution (CAMODE) model is presented. CAMODE utilizes the advantages of chaotic sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. Secondly, opposition-based Multiple Objective Differential Evolution (OMODE) model is presented. OMODE employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. Finally, a new hybrid multiple-objective artificial bee colony with differential evolution (MOABCDE) model is proposed. The proposed algorithm integrates crossover operations from differential evolution (DE) with the original artificial bee colony (ABC) in order to balance the exploration and exploitation phases of the optimization process. Numerous real construction case studies including time-cost-quality tradeoff, time-cost tradeoffs in resource-constrained, time-cost-labor utilization tradeoff and time-cost-environment impact tradeoff problems are used to demonstrate the proposed models. The proposed models are validated by comparing with current widely used multiple objective algorithms, including the non-dominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC) and previous works via comparison indicators and hypothesis test. Experimental results obtained from the proposed models confirm that using the newly established models can be a highly beneficial for decision-makers when solving various problems in the field of construction management.
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Ishak, Mohd Yusoff. "Predictive modelling of eutrophication and algal bloom formation in tropical lakes." Thesis, 2012. http://hdl.handle.net/2440/78097.
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My original contribution to knowledge is the successful application of two modelling paradigms 1) SALMO-PLUS process based model and 2) HEA data driven model to tropical lakes of different morphometry and trophic status. The application of SALMO-PLUS to tropical lakes involves utilising the SALMO-OO model structure for optimisation. This was followed by multi objective parameter optimisation on selected parameters to seek the optimum parameter values that can model the algal dynamics and state variables fluctuations in the tropical lakes to an
acceptable magnitude and peaks. SALMO-PLUS is another version SALMO-OO with capability to run optimisation by means of particle swarm optimisation (PSO) method. SALMO-OO has been used as a research tool over a number of lakes with different trophic states and mixing conditions to simulate algal succession and respond to ecosystem dynamic. SALMO-OO is driven by process-based differential equations and works by utilizing a library of three phytoplankton growth and three grazing process models. Evolutionary algorithms (EA) are bio-inspired adaptive methods which mimic processes of biological evolution, natural selection and genetic variation such as cross-over and mutation to develop solutions to complex computational problems (Recknagel et al, 2006). HEA is designed for rule discovery in water quality timeseries (Cao et al., 2006b) and is capable of forecasting potential algal population dynamics and outbreaks in water bodies. The SALMO-PLUS model was applied for simulating the state variables of selected lakes (Lake Kenyir, Lake Penang, Saidenbach Reservoir, Roodeplaat Dam and South Para Reservoir). Measured data from the year 2005 and 1992 were used for Lake Penang and Lake Kenyir respectively. The HEA was applied for predicting the Chl-a and algal biovolume abundance on tropical lakes (Lake Putrajaya, Lake Penang and Lake Kenyir) in Malaysia. This study discusses the application of SALMO-PLUS and HEA towards tropical lakes eutrophication management. The results of application of SALMO-PLUS on tropical lakes are presented, simulating response of the phytoplankton community to fluctuation in nutrient loading, light availability and hydrological aspect in the water bodies. Results of applying HEA on tropical lakes are also interpreted in the context of empirical and causal knowledge on Chl-a and algal biovolumes dynamics under tropical lake water quality conditions by means of rule-based model. Results for both Lake Kenyir and Penang showed that SALMO-PLUS were able to predict annual average trends not only for chlorophyll-a but also other state variables and algal functional groups. Simulated state variables namely Chl-a, N and P showed good agreement with field observations data for both lakes. Despite the fact that this is the first time SALMO-PLUS been used for tropical lakes and the limited data availability from this region, the simulated values of biological and nutrient state variables match reasonably with measured data. Outcomes from SALMO-PLUS simulation show consistent compliance with algal community assembly obtained from other researchers. The HEA achieved reasonable accuracy in predicting timing and magnitudes of algal blooms up to 7-days-ahead. The HEA proved to be most efficient in modelling and predicting seasonal dynamics of chlorophyll-a and algal biovolumes. A sensitivity analysis conducted for Lake Penang revealed that algal abundance is not only driven by physical and chemicals characteristics of the water body but also by impact of inorganic substances in the water that contributes to high level of chemical oxygen demand in the water as well. In addition, this study has successfully implemented a new process model from Law et al. (2009) consisting algal growth, algal grazing, zooplankton growth and zooplankton mortality functions into the SALMO-OO simulation library. Combination of this new process models were tested on dataset from Lake Kenyir, Lake Penang, Saidenbach Reservoir and Roodeplaat Dam within the simulation library to discover the optimal model structures for respective water bodies. Even though the new process model was not selected in complete totality as the optimal model structure for any of the test lakes, the addition has added another alternative for water body simulation in SALMO-OO process library. Based on these forecasting results, both SALMO-PLUS and HEA have showed potential for utilisation in early warning and strategic control of algal blooms in tropical freshwater lakes. The generic nature of HEA forecast model was also observed when tested for forecasting algal biovolume for merged data of similar lake ecosystem category. Results from merged Lake Kenyir and Lake Penang data showed reasonable accuracy in predicting the timing and magnitudes of algal blooms up to 7-days-ahead. Addition of the new process model from Law et al. (2009) into the SALMO-PLUS simulation library has also expanded the alternative for lake category simulation to give a more comprehensive decision support tool for lake and reservoir management. This study has also affirmed the generality and flexibility of SALMO-PLUS for usage in tropical lakes modelling. SALMO-PLUS was observed to be capable of simulating simultaneous seasonal fluctuations in algal growth and nutrients (phosphate and nitrate) making it valuable for forecasting the impacts of various simulated scenarios for various lake management regimes.Thesis (Ph.D.) -- University of Adelaide, School of Earth and Environmental Sciences, 2012
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Lin, Chi-Huai, and 林淇淮. "Security-Constrained Economic Operation of Power Systems Using Hybrid Differential Evolution Algorithm." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/72823003419374894860.
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碩士國立中正大學
電機工程研究所
92
With the trend of deregulation of electric powe industries, the demand of quality of electricity supply rises.The electric power utility always pursues stable and reliable electricity supply to satisfy the requirements of consumers in a way of economic and secure operation for the power system.This thesis is an attempt to explore the security-constrained economic operation of power systems. The problem of security-constrained economic operation of power systems is an optimization problem, which looks for both the economic power generation and the appropriate reactive power compensation. Real power outputs of generation units are economically dispatched to reach a minimum generation cost, whereas reactive power outputs of generation units and capacitor banks are appropriately dispatched to compensate the reactive power requirement of the system and control the bus voltage as well as line flows. The problem under study is an optimization problem and is to be solved using the hybrid differential evolution (HDE) method. HDE is computationally simple, which provides robust-search capability in a huge solution space. It employs two additional operations than the previous differential evolution (DE), these two operations are the acceleration technique and the migration technique. The acceleration technique can increase the convergence speed and the migration technique can avoid falling into a local solution and achieve the optimal solution. Application of the proposed approach is demonstrated and verified using two application systems including a 9-bus and a 26-bus systems.
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You, Ming-Sian, and 尤銘賢. "Cloud based Hybrid Evolution Algorithm for NP-Complete Pattern in Nurse Scheduling Problem." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/vv9bu5.
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碩士國立虎尾科技大學
資訊工程系碩士班
104
In this thesis, the Cloud based Hybrid evolution algorithm for NP-Complete Pattern in Nurse Scheduling Problem (NSP) is proposed as the Software Computing as a Service (SCaaS). Due to the low birth rate, the human resource becomes the limited resource for job assignment. To find the optimal solution for staff scheduling becomes an important issue. The proposed system follows the definition of NSP and recognizes the possible problem of NP-Complete Pattern. Only the pattern is recognized as the NSP optimal problem, the proposed system can find the optimal solution. Then, the different types of evolutionary algorithm in evolution steps are integrated. Based on the proposed Feedback Assistance method, the suitable evolution steps of the evolutionary algorithm can be dynamically decided and executed. Similar to the Tasktracker and Jobtracker in cloud, all the computing load can be divided and distributed. The simulation results show that the proposed hybrid evolution algorithm can find the optimal solution with about 50% less evolution generations.
Books on the topic "Hybrid Evolution Algorithms"
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Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.
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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
Book chapters on the topic "Hybrid Evolution Algorithms"
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Mo, W., S. U. Guan, and Sadasivan K. Puthusserypady. "A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy." In Hybrid Evolutionary Algorithms, 101–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_5.
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Aranguren, Itzel, Arturo Valdivia, and Marco A. Pérez. "Segmentation of Magnetic Resonance Brain Images Through the Self-Adaptive Differential Evolution Algorithm and the Minimum Cross-Entropy Criterion." In Applications of Hybrid Metaheuristic Algorithms for Image Processing, 311–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40977-7_14.
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Noghanian, Sima, Abas Sabouni, Travis Desell, and Ali Ashtari. "Global Optimization: Differential Evolution, Genetic Algorithms, Particle Swarm, and Hybrid Methods." In Microwave Tomography, 39–61. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0752-6_3.
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Fefelova, Iryna, Andrey Fefelov, Volodymyr Lytvynenko, Róża Dzierżak, Iryna Lurie, Nataliia Savina, Mariia Voronenko, and Svitlana Vyshemyrska. "Protein Tertiary Structure Prediction with Hybrid Clonal Selection and Differential Evolution Algorithms." In Advances in Intelligent Systems and Computing, 673–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26474-1_47.
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Fu, Wenlong, Mark Johnston, and Mengjie Zhang. "Hybrid Particle Swarm Optimisation Algorithms Based on Differential Evolution and Local Search." In AI 2010: Advances in Artificial Intelligence, 313–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17432-2_32.
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Zhao, Zhan-Fang, Kun-Qi Liu, Xia Li, You-Hua Zhang, and Shu-Lin Wang. "Research on Hybrid Evolutionary Algorithms with Differential Evolution and GUO Tao Algorithm Based on Orthogonal Design." In Lecture Notes in Computer Science, 78–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14922-1_11.
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Saif, Faten A., Rohaya Latip, M. N. Derahman, and Ali A. Alwan. "Hybrid Meta-heuristic Genetic Algorithm: Differential Evolution Algorithms for Scientific Workflow Scheduling in Heterogeneous Cloud Environment." In Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3, 16–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18344-7_2.
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Li, Xia, Kunqi Liu, Lixiao Ma, and Huanzhe Li. "A Concurrent-Hybrid Evolutionary Algorithms with Multi-child Differential Evolution and Guotao Algorithm Based on Cultural Algorithm Framework." In Advances in Computation and Intelligence, 123–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16493-4_13.
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Chang, Le, and Jiaben Yang. "MEBRL: Memory-Evolution-Based Reinforcement Learning Algorithm of MAS." In Hybrid Information Systems, 449–58. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_32.
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Mollinetti, Marco Antônio Florenzano, Daniel Leal Souza, Rodrigo Lisbôa Pereira, Edson Koiti Kudo Yasojima, and Otávio Noura Teixeira. "ABC+ES: Combining Artificial Bee Colony Algorithm and Evolution Strategies on Engineering Design Problems and Benchmark Functions." In Hybrid Intelligent Systems, 53–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27221-4_5.
Full textConference papers on the topic "Hybrid Evolution Algorithms"
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Kromer, Pavel, Václav Snasel, Jan Platos, and Ajith Abraham. "Optimization of Turbo Codes by Differential Evolution and Genetic Algorithms." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.289.
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T. Basokur, A., and I. Akca. "Hybrid Genetic Algorithms Derived from the Evolution Theories." In 4th Congress of the Balkan Geophysical Society. European Association of Geoscientists & Engineers, 2005. http://dx.doi.org/10.3997/2214-4609-pdb.26.o10-02.
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Nogueira de Sousa, Gustavo, and Omar Andres Carmona Cortes. "On a Cooperative Hybrid Algorithm Based on Harmony Search and Differential Evolution for Numerical Optimization." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p214-220.
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Hybrid algorithms aim to mix features from two or more evolutionary/swarm algoprove both the exploration and exploitationabilities of the algorithm. Generally, hybrid algorithms prrithmsto imesent the same quality of solution than the canonical ones,in the worst case scenario. However, it is common that hybridalgorithms present better outcomes than the canonical ones. Inthis context, this paper proposes a cooperative hybrid algorithmbased on Harmony Search and Differential Evolution named HS-DE.The algorithm has been tested in five benchmark functions wellknown in the literature. Results have shown that HS-DE presentsbetter solutions than Genetic Algorithms, Particle Swarm Optimization,Differential Evolution, and Harmony Search in all benchmarkfunctions.
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Olatunji, Obafemi, Stephen Akinlabi, Nkosinathi Madushele, Paul Adedeji, and Samuel Fatoba. "Evolution Algorithms and Biomass Properties Prediction: A Review." In ASME 2019 Power Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/power2019-1826.
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Abstract The complexity of real-world applications of biomass energy has increased substantially due to so many competing factors. There is an ongoing discussion on biomass as a renewable energy source and its cumulative impact on the environment vis-a-vis water competition, environmental pollution and so on. This discussion is coming at a time when evolutionary algorithms and its hybrid forms are gaining traction in several applications. In the last decade, evolution algorithms and its hybrid forms have evolved as a significant optimization and prediction technique due to its flexible characteristics and robust behaviour. It is very efficient means of solving complex global optimization problems. This article presents the state-of-the-art review of different types of evolutionary algorithms, which have been applied in the prediction of major properties of biomass such as elemental compositions and heating values. The governing principles, applications, merits, and challenges associated with this technique are elaborated. The future directions of the research on biomass properties prediction are discussed.
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Li, Ling-po, and Ling Wang. "Hybrid algorithms based on harmony search and differential evolution for global optimization." In the first ACM/SIGEVO Summit. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1543834.1543871.
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Chen, Xianqi, Wen Yao, Yong Zhao, Xiaoqian Chen, Jun Zhang, and Yazhong Luo. "The Hybrid Algorithms Based on Differential Evolution for Satellite Layout Optimization Design." In 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018. http://dx.doi.org/10.1109/cec.2018.8477969.
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Cheng, Shuo, and Mian Li. "Multi-Objective Robust Optimization Using Differential Evolution and Sequential Quadratic Programming." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12293.
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Multi-Objective Robust Optimization (MORO) can find Pareto solutions to multi-objective engineering problems while keeping the variation of the solutions being within an acceptable range when parameters vary. While the literature reports on many techniques in MORO, few papers focus on the implementation of Multi-Objective Differential Evolution (MODE) for robust optimization and the performance improvement of solutions. In this paper, MODE is first modified and implemented for robust optimization, formulating a new MODE-RO algorithm. To improve the solutions’ quality of MODE-RO, a new hybrid MODE-SQP-RO algorithm is further proposed, where Sequential Quadratic Programming (SQP) is incorporated to enhance the local search. In the hybrid algorithm, two criteria, indicating the convergence speed of MODE-RO and the switch between MODE and SQP are proposed respectively. One numerical and one engineering examples are tested to demonstrate the applicability and performance of the proposed algorithms. The results show that MODE-RO is effective in solving Multi-Objective Robust Optimization problems; while on the average, MODE-SQP-RO significantly improves the quality of robust solutions with comparable numbers of function evaluations.
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Ustun, Deniz, and Ali Akdagli. "A study on the performance of the hybrid optimization method based on artificial bee colony and differential evolution algorithms." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090346.
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Abubakar, Abba A., Abul Fazal M. Arif, Khaled S. Al-Athel, and S. Sohail Akhtar. "Prediction of Residual Stress and Damage in Thermal Spray Coatings Using Hybrid Computational Approach." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86504.
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Due to the multilayered pattern of coating deposition, numerical prediction of residual stress and damage in thermal spray coatings (TSCs) has been challenging. Several numerical approaches previously used failed to capture essential aspects such as deposition stress build-up, presence of heterogeneities, and influence of process parameters. In the present study, a hybrid computational approach which combines “point cloud” (PC) and finite elements (FE) has been used to model the spray process as well as the evolution of residual stress and damage. Smooth particle hydrodynamics (SPH) is used to model multiple droplets deposition and associated deformation on PC. Then, several recent algorithms (for point cloud processing) are used to convert the deformed droplets (in form of PC) into FE domains (i.e. splats). The FE mesh of deposited splats is used for thermo-mechanical finite element analysis where the evolution of temperature, residual stress and damage is predicted on simulated coating microstructure. By comparing our numerical results with that of previous works, the hybrid approach has been found to be a viable tool for quantitative assessment of residual stresses and failure in TSCs.
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Galski, Roberto Luiz, Heitor Patire Ju´nior, Fabiano Luis de Sousa, Jose´ Nivaldo Hinckel, Pedro Lacava, and Fernando Manuel Ramos. "GEO + ES Hybrid Optimization Algorithm Applied to the Parametric Thermal Model Estimation of a 200N Hydrazine Thruster." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47584.
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In the present paper, a hybrid version of the Generalized Extremal Optimization (GEO) and Evolution Strategies (ES) algorithms [1], developed in order to conjugate the convergence properties of GEO with the self-tuning characteristics present in the ES, is applied to the estimation of the temperature distribution of the film cooling near the internal wall of a thruster. The temperature profile is determined through an inverse problem approach using the hybrid. The profile was obtained for steady-state conditions, were the external wall temperature along the thruster is considered as a known input. The Boltzmann’s equation parameters [2], which define the cooling film temperature profile, are the design variables. Results using simulated data showed that this approach was efficient in recuperating those parameters. The approach showed here can be used on the design of thrusters with lower wall temperatures, which is a desirable feature of such devices.