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hybrid algorithm

در نشریات گروه فناوری اطلاعات
تکرار جستجوی کلیدواژه hybrid algorithm در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه hybrid algorithm در مقالات مجلات علمی
  • Somaye Mohammadpor, Maryam Rahmaty*, Fereydon Rahnamay Roodposhti, Reza Ehtesham Rasi

    <span style="font-family:Calibri,sans-serif">In this article, the modeling and solution of a cryptocurrency capital portfolio optimization problem has been discussed. The presented model, which is based on Markowitz's mean-variance method, aims to maximize the non-deterministic internal return and minimize the cryptocurrency investment risk. A combined PSO and SCA algorithm was used to optimize this two-objective model. The results of the investigation of 40 investment portfolios in a probable state showed that with the increase in the internal rate of return, the investment risk increases. So in the optimistic state, there is the highest internal rate of return and in the pessimistic state, there is the lowest investment risk. Investigations of the investment portfolio in the probable state also showed that more than 80% of the investment was made to optimize the objective functions in 5 cryptocurrencies BTC, ETH, USTD, ADA, and XRP. So in the secondary analysis, it was observed that in the case of investing in the top 5 cryptocurrencies, the average internal rate of return increased by 9.92%, and the average investment risk decreased by 0.1%.</span></span></span>

    Keywords: Cryptocurrency Investment Portfolio Optimization, Non-Deterministic Internal Rate Of Return, Hybrid Algorithm
  • Sosan Sarbazfard, Ahmad Jafarian *
    In this paper, a new and an e ective combination of two metaheuristic algorithms, namely Fire y Algorithm and the Di erential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Di erential Evolution (DE) and Fire y Algorithm (FA). Fire y algorithm is the nature- inspired algorithm which has its roots in the light intensity attraction process of re y in the nature. Di erential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are e ective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in nding the best solution and DE needs more iteration to nd proper solution. As a result, this proposed method has been designed to cover each algorithm de ciencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and ndings showed that HFADE is a more preferable and e ective method in solving the high-dimensional functions.
    Keywords: Differential Evolution, Firefly Algorithm, Global Optimization, Hybrid Algorithm
  • Mostafa Moradi
    In this paper by combining caching and replication techniques proposed a hybrid heuristic method based on the greedy algorithm to use the benefit of each other techniques. The algorithm in each interaction compares all the contents and one that made the best benefit value is selected for replication. The hybrid approach tested in a simulation environment and the results show that hybrid algorithm again stand-alone replication, reduced the average response time by 42% and compared to the pure caching, saving up 23% user requests time.
    Keywords: Content Delivery Network (CDN), Hybrid Algorithm, Replica Placement
  • Ali Safari Mamaghani, Kayvan Asghari, Mohammad Reza Meybodi
    Evolutionary algorithms are some of the most crucial random approaches to solve the problems, but sometimes generate low quality solutions. On the other hand, Learning automata are adaptive decision-making devices, operating on unknown random environments, So it seems that if evolutionary and learning automaton based algorithms are operated simultaneously, the quality of results will increase sharply and the algorithm is likely to converge on best results very quickly. This paper contributes an algorithm based on learning automaton to improve the evolutionary algorithm for solving a group of NP problems. It uses concepts of machine learning in search process, and increases efficiency of evolutionary algorithm (especially genetic algorithm). In fact, the algorithm is prevented from being stuck in local optimal solutions by using learning automaton. Another positive point of the hybrid algorithm is its noticeable stability since standard division of results, which is obtained by different executions of algorithm, is low; that is, the results are practically the same. Therefore, as the proposed algorithm is used for a set of well-known NP problems and the results are very suitable it can be considered as a precise and reliable technique to solve the problems.
    Keywords: Learning Automaton, Genetic Algorithm, Hybrid Algorithm, NP Problems
  • Soroor Sarafrazi, Hossein Nezamabadi, Pour
    In recent years, hybrid algorithms (HAs) have been successfully applied for solving decision and optimization problems. Nevertheless, selecting good algorithms for hybridization has been a crucial issue in HAs. In this paper, a new hybrid algorithm composed of gravitational search algorithm (GSA) and the proposed adaptive stochastic search (ASS) method is introduced. These effective search algorithmsprovide a good trade-off between exploration and exploitation. The performance of the proposed HA is evaluated in the field of numerical function optimization on 23 standard benchmark functions and also on a practical optimization problem, optimal approximation of linear systems. The results are compared with those of some well-known HAs and confirm the efficiency of the proposed method in solving various nonlinear test functions.
    Keywords: Hybrid algorithm, swarm intelligence, gravitational search algorithm, adaptive stochastic search, numerical function optimization
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