• Proceedings of the KIEE Conference
• /
• 2002.07a
• /
• pp.121-123
• /
• 2002

In this paper, we preposed a chaos optimization method to reduce computational effort and enhance optimality of the solution in feeder reconfiguration problem. Chaos method in optimization problem searches the global optimal solution on the regularity of chaotic motions and more easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos optimization method is tested on 15 buses and 32 buses distribution systems, and the test results indicate that it is able to determine appropriate switching options for global optimum configuration with less computation.

• Journal of Communications and Networks
• /
• v.18 no.5
• /
• pp.784-795
• /
• 2016

In this paper, we investigate the resource allocation problem in time-varying heterogeneous wireless networks (HetNet) with multi-homing user equipments (UE). The stochastic optimization model is employed to maximize the network utility, which is defined as the difference between the HetNet's throughput and the total energy consumption cost. In harmony with the hierarchical architecture of HetNet, the problem of stochastic optimization of resource allocation is decomposed into two subproblems by the Lyapunov optimization theory, associated with the flow control in transport layer and the power allocation in physical (PHY) layer, respectively. For avoiding the signaling overhead, outdated dynamic information, and scalability issues, the distributed resource allocation method is developed for solving the two subproblems based on the primal-dual decomposition theory. After that, the adaptive resource allocation algorithm is developed to accommodate the timevarying wireless network only according to the current network state information, i.e. the queue state information (QSI) at radio access networks (RAN) and the channel state information (CSI) of RANs-UE links. The tradeoff between network utility and delay is derived, where the increase of delay is approximately linear in V and the increase of network utility is at the speed of 1/V with a control parameter V. Extensive simulations are presented to show the effectiveness of our proposed scheme.

• Journal of Internet Computing and Services
• /
• v.22 no.1
• /
• pp.1-11
• /
• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.23 no.5
• /
• pp.420-427
• /
• 2004

This paper presents real time implementation of 4kbps EHSX (Enhanced Harmonic Stochastic Excitation) speech coder that combines the harmonic vector excitation coding with time-separated transition coding. The harmonic vector excitation coding uses the harmonic excitation coding for voiced frames and used the vector excitation coding with the structure of analysis-by-synthesis for unvoiced frames, respectively. For transition frames mixed with voiced and unvoiced signal, we use the time-separated transition coding. In this paper. we present the optimization methods of implementation speech coder on the EMS320C6701/sup (R)/ DSP. To reduce the complex for real-time implementation. we perform the optimization method in algorithm by replacing the complex sinusoidal synthesis method with IFFT. and we apply fully pipelines hand assembly coding after converting it from floating source to fixed source. To generate a more efficient code. we also make use or the available EMS320C6701/sup (R)/ resources such as Fastest67x library and memory organization.

• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.348-351
• /
• 1993

The simulated annealing(SA) algorithm is a stochastic strategy for search of the ground state and a powerful tool for optimization. based on the anneal ins process used for the crystallization in physical systems. It's main disadvantage is the long convergence time. Therefore, this paper shows that the new algorithm using SA can be applied to reduce the computation time. This idea has been used to solve the estimation problem of the nonlinear parameter.

• Journal of the Institute of Convergence Signal Processing
• /
• v.13 no.4
• /
• pp.207-210
• /
• 2012

Genetic algorithms(GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of GA, this paper proposes an adaptive partitioning-based genetic algorithm. The partitioning method, which enables GA to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and the optimization of fuzzy controller for the control of an inverted pendulum.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.2
• /
• pp.483-503
• /
• 2015

This paper analyzes the optimal user density and power allocation for Device-to-Device (D2D) communication underlaying cellular networks on multiple bands with the target of maximizing the D2D transmission capacity. The entire network is modeled by Poisson point process (PPP) which based on stochastic geometry. Then in order to ensure the outage probabilities of both cellular and D2D communication, a sum capacity optimization problem for D2D system on multiple bands is proposed. Using convex optimization, the optimal D2D density is obtained in closed-form when the D2D transmission power is determined. Next the optimal D2D transmission power is obtained in closed-form when the D2D density is fixed. Based on the former two conclusions, an iterative algorithm for the optimal D2D density and power allocation on multiple bands is proposed. Finally, the simulation results not only demonstrate the D2D performance, density and power on each band are constrained by cellular communication as well as the interference of the entire system, but also verifies the superiority of the proposed algorithm over sorting-based and removal algorithms.

• Journal of Sensor Science and Technology
• /
• v.25 no.6
• /
• pp.431-434
• /
• 2016

Currently, the radial basis function network (RBFN) and various other neural networks are employed to classify gases using chemical sensors arrays, and their performance is steadily improving. In particular, the identification performance of the RBFN algorithm is being improved by optimizing parameters such as the center, width, and weight, and improved algorithms such as the radial basis function network-stochastic gradient (RBFN-SG) and radial basis function network-normalized stochastic gradient (RBFN-NSG) have been announced. In this study, we optimized the number of centers, which is one of the parameters of the RBFN-NSG algorithm, and observed the change in the identification performance. For the experiment, repeated measurement data of 8 samples were used, and the elbow method was applied to determine the optimal number of centers for each sample of input data. The experiment was carried out in two cases(the only one center per sample and the optimal number of centers obtained by elbow method), and the experimental results were compared using the mean square error (MSE). From the results of the experiments, we observed that the case having an optimal number of centers, obtained using the elbow method, showed a better identification performance than that without any optimization.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.32 no.6
• /
• pp.49-55
• /
• 2004

The increase of computing power makes stochastic optimization algorithms available in structural design. One of the stochastic algorithms, simulated annealing algorithm, has been applied to various structural optimization problems. By applying several cooling schedules such as simulated annealing (SA), Boltzmann annealing (BA), fast annealing (FA) and adaptive simulated annealing (ASA), truss structures are optimized to improve the quality of objective functions and reduce the number of function evaluations. In this paper, many cooling parameters have been applied to the cooling schedule of ASA. The influence of cooling parameters is investigated to find the rules of thumb for using ASA. Tn addition, the cooling schedule combined with BA and ASA is applied to the optimization of ten bar-truss and twenty five bar-truss structure.

• The KIPS Transactions:PartB
• /
• v.18B no.1
• /
• pp.39-44
• /
• 2011

In this paper, we propose a method to improve the Firefly Algorithm(FA) introduced by Xin-She Yang, recently. We design and analyze the improved firefly algorithm based on the heuristic. We compare the FA with the Particle Swarm Optimization (PSO) which the problem domain is similar with the FA in terms of accuracy, algorithm convergence time, the motion of each particle. The compare experiments show that the accuracy of FA is not worse than PSO's, but the convergence time of FA is slower than PSO's. In this paper, we consider intuitive reasons of slow convergence time problem of FA, and propose the improved version of FA using a partial mutation heuristic based on the consideration. The experiments using benchmark functions show the accuracy and convergence time of the improved FA are better than them of PSO and original FA.