• Korean Management Science Review
• /
• v.15 no.1
• /
• pp.77-85
• /
• 1998

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solutions of a multiple objective linear program. Since the efficient set is in general a nonconvex set, problem (P) can be classified as a global optimization problem. Perhaps due to its inherent difficulty, relatively few attempts have been made to solve problem (P) in spite of the potential benefits which can be obtained by solving problem (P). The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.10
• /
• pp.5013-5034
• /
• 2019

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.

• Korean Journal of Environmental Biology
• /
• v.22 no.1
• /
• pp.167-170
• /
• 2004

A self-directing optimization procedure was applied to determine the best environmental factors in operating the bioreactor. The self-directing optimization process was employed to determine the best conditional combination of multi parameters, pH, temperature, aeration rate and mixing rate toy maximal production of chitinase by Trichoderma harzianum SJG-99721 in batch mode fermentation. Among these factors, the parameters of pH and aeration rate were found to be particularly important on mycellial growth and chitinase activity. pH 4.89, an aeration rate of 3.22 ι per minute and an agitation rate of 225 rpm was found to be the best combination. By the optimization, chitinase activity was dramatically increased from an initial value of 4.221 U under basic conditions to n final value of 16.825 U.

• Nonlinear Functional Analysis and Applications
• /
• v.26 no.1
• /
• pp.65-70
• /
• 2021

We characterize the solution set for a second-order cone linear fractional optimization problem (P). We present sequential Lagrange multiplier characterizations of the solution set for the problem (P) in terms of sequential Lagrange multipliers of a known solution of (P).

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.5
• /
• pp.155-162
• /
• 2015

This paper proposes a deterministic optimization algorithm to solve economic load dispatch problem with quadratic convex fuel cost function. The proposed algorithm primarily partitions a generator with prohibited zones into multiple generators so as to place them afield the prohibited zone. It then sets initial values to $P_i{\leftarrow}P_i^{max}$ and reduces power generation costs of those incurring the maximum unit power cost. It finally employs a swap optimization process of $P_i{\leftarrow}P_i-{\beta}$, $P_j{\leftarrow}P_j+{\beta}$ where $_{max}\{F(P_i)-F(P_i-{\beta})\}$ > $_{min}\{F(P_j+{\beta})-F(P_j)\}$, $i{\neq}j$, ${\beta}=1.0,0.1,0.01,0.001$. When applied to 3 different 15-generator cases, the proposed algorithm has consistently yielded optimized results compared to those of heuristic algorithms.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2010.11a
• /
• pp.631-632
• /
• 2010

• ETRI Journal
• /
• v.37 no.5
• /
• pp.906-916
• /
• 2015

High-speed I/O channels require adaptive techniques to optimize the settings for filter tap weights at decision feedback equalization (DFE) read channels to compensate for channel inter-symbol interference (ISI) and crosstalk from multiple adjacent channels. Both ISI and crosstalk tend to vary with channel length, process, and temperature variations. Individually optimizing parameters such as those just mentioned leads to suboptimal solutions. We propose a joint optimization technique for crosstalk cancellation (XTC) at DFE to compensate for both ISI and XTC in high-speed I/O channels. The technique is used to compensate for between 15.7 dB and 19.7 dB of channel loss combined with a variety of crosstalk strengths from $60mV_{p-p}$ to $180mV_{p-p}$ adaptively, where the transmit non-return-to-zero signal amplitude is a constant $500mV_{p-p}$.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.54 no.2
• /
• pp.61-66
• /
• 2005

This paper presents an unified method which solves real and reactive power dispatch problems for the economic operation of power systems using evolutionary computation such as genetic algorithms(GA), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most of these approaches have the common defect of being caught to a local minimum solution. The proposed methods, applied to the IEEE 30-bus system, were run for 10 other exogenous parameters and composed of P-optimization module and Q-optimization module. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.1
• /
• pp.188-210
• /
• 2022

As a promising technique to support tremendous numbers of Internet of Things devices and a variety of applications efficiently, mobile edge computing (MEC) has attracted extensive studies recently. In this paper, we consider a MEC-assisted peer-to-peer (P2P) mobile communication system where MEC servers are deployed at access points to accelerate the communication process between mobile terminals. To capture the tradeoff between the time delay and the energy consumption of the system, a cost function is introduced to facilitate the optimization of the computation and communication resources. The formulated optimization problem is non-convex and is tackled by an iterative block coordinate descent algorithm that decouples the original optimization problem into two subproblems and alternately optimizes the computation and communication resources. Moreover, the MEC-assisted P2P communication system is compared with the conventional P2P communication system, then a condition is provided in closed-form expression when the MEC-assisted P2P communication system performs better. Simulation results show that the advantage of this system is enhanced when the computing capability of the receiver increases whereas it is reduced when the computing capability of the transmitter increases. In addition, the performance of this system is significantly improved when the signal-to-noise ratio of hop-1 exceeds that of hop-2.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.392-395
• /
• 1995

This paper presents a time efficient method for determining a sequence of locations of a mobile manipulator that facilitates tracking of continuous path in cluttered environment. Given the task trajectory in the form of octree data structure, the algorithm performs characterization of task space and subsequent multistage optimization process to determine task feasible locations of the robot. Firstly, the collision free portion of the trajectory is determined and classified according to uniqueness domains of the inverse kinematics solutions. Then by implementing the extent of task feasible subspace into an optimization criteria, a multistage optimization problem is formulated to determines the task feasible locations of the mobile manipulator. The effectiveness of the proposed method is shown through a simulation study performed for a 3-d.o.f. manipulator with generic kinematic structure.