• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.721-726
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• 2012

In this paper, we proposed an efficient algorithm based on SPFA(shortest path faster algorithm), which is an improved the Bellman-Ford algorithm. The Bellman-Ford algorithm can be used on graphs with negative edge weights unlike Dijkstra's algorithm. And SPFA algorithm used a queue to store the nodes, to avoid redundancy, though the Bellman-Ford algorithm takes a long time to update the nodes table. In this improved algorithm, an adjacency list is also used to store each vertex of the graph, applying dynamic optimal approach. And a queue is used to store the data. The improved algorithm can find the optimal path by continuous relaxation operation to the new node. Simulations to compare the efficiencies for Dijkstra's algorithm, SPFA algorithm and improved Bellman-Ford were taken. The result shows that Dijkstra's algorithm, SPFA algorithm have almost same efficiency on the random graphs, the improved algorithm, although the improved algorithm is not desirable, on grid maps the proposed algorithm is very efficient. The proposed algorithm has reduced two-third times processing time than SPFA algorithm.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.717-726
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• 2006

This study is to investigate the MAE of prediction value by collaborative filtering algorithm originated by GroupLens and improved algorithm. To decrease the MAE on the collaborative recommender system on user based, this research proposes the improved algorithm, which reduces the possibility of over estimation of active user's preference mean collaboratively using other user’s preference mean. The result shows the MAE of prediction by improved algorithm is better than original algorithm, so the active user's preference mean used in prediction formula is possibly over estimated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1649-1665
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• 2021

In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.213-216
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• 1998

This paper proposes improved algorithm for multi-user detection in DS-CDMA. Each of algorithm is based on CMA algorithm. Improved LMS-CMS and LMAD-CMA are combined to macthed filter. Simulations results shown that Improved LMAD-CMA algorithm has a higher capacity than MOE in steady-state convergence properties.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3A
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• pp.247-260
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• 2007

This paper solves the problem of finding a suitable sub-channel and power joint allocation method for multiple users in 802.16e OFDMA/TDD cellular systems. The joint allocation is thatfirstly the sub-channel is allocated to the users and then suitable power is allocated. We propose a FASA (Fairness insured Aggressive Sub-channel Allocation) algorithm which is a dynamic channel allocation algorithm considering all users' channel state information conditionally to maximize fairness and throughput. The improved CHC algorithm, which is dynamic power allocation algorithm, is also proposed in this paper The Improved CHC algorithm collects the extra of the downlink transmit power and then re-allocates it to other users. Simulation results show that the proposed improved CHC algorithm additionally increases the fairness and sector throughput.

• ETRI Journal
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• v.39 no.6
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• pp.782-793
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• 2017

This paper considers the use of the Partial Transmit Sequence (PTS) technique to reduce the Peak-to-Average Power Ratio (PAPR) of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS) is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search-based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.116-126
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• 2019

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.