• Korean Journal of Mathematics
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• v.4 no.1
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• pp.7-16
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• 1996

The optimization problems with quadratic constraints often appear in various fields such as network flows and computer tomography. In this paper, we propose an algorithm for solving those problems and prove the convergence of the proposed algorithm.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.16-24
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• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.656-666
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• 2014

In delay tolerant networks (DTNs), delay is inevitable; thus, making better use of buffer space to maximize the packet delivery rate is more important than delay reduction. In DTNs, epidemic routing is a well-known routing protocol. However, epidemic routing is very sensitive to buffer size. Once the buffer size in nodes is insufficient, the performance of epidemic routing will be drastically reduced. In this paper, we propose a buffer scheme to optimize the performance of epidemic routing on the basis of the Lagrangian and dual problem models. By using the proposed optimal buffer scheme, the packet delivery rate in epidemic routing is considerably improved. Our simulation results show that epidemic routing with the proposed optimal buffer scheme outperforms the original epidemic routing in terms of packet delivery rate and average end-to-end delay. It is worth noting that the improved epidemic routing needs much less buffer size compared to that of the original epidemic routing for ensuring the same packet delivery rate. In particular, even though the buffer size is very small (e.g., 50), the packet delivery rate in epidemic routing with the proposed optimal buffer scheme is still 95.8%, which can satisfy general communication demand.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.4
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• pp.800-818
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• 2013

Supporting video streaming over multi-hop wireless networks is particularly challenging due to the time-varying and error-prone characteristics of the wireless channel. In this paper, we propose a joint optimization scheme for video streaming over multi-hop wireless networks. Our coding scheme, called Joint Source/Network Coding (JSNC), combines source coding and network coding to maximize the video quality under the limited wireless resources and coding constraints. JSNC segments the streaming data into generations at the source node and exploits the intra-session coding on both the source and the intermediate nodes. The size of the generation and the level of redundancy influence the streaming performance significantly and need to be determined carefully. We formulate the problem as an optimization problem with the objective of minimizing the end-to-end distortion by jointly considering the generation size and the coding redundancy. The simulation results demonstrate that, with the appropriate generation size and coding redundancy, the JSNC scheme can achieve an optimal performance for video streaming over multi-hop wireless networks.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.130-138
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• 2023

The Smart grids are considered as multi-disciplinary power systems where the communication networks are highly employed. This paper presents optimal wide area measurement system (WAMS) configuration in Nordic power system. The transition from SCADA to WAMS becomes now trend in all power systems to ensure higher reliability and data visibility. The optimization applied in this research considered the geographical regions of the Nordic power system. The research considered all the devices of WAMS namely phasor measurement units (PMUs), phasor data concentrators (PDCs) and communication links. The study also presents two scenarios for optimal WAMS namely base case and N-1 contingency as different operating conditions. The result of this research presents technical and financial results for WAMS configuration in a real power system. The optimization results are performed using MATLAB 2017a software application.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.261-273
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• 2022

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.