• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.843-859
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• 2024

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.2
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• pp.13-20
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• 2021

Placement is a very important step in the VLSI physical design process. It is the problem of placing circuit modules to optimize the circuit performance and reliability of the circuit. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for circuit placement include the cluster growth, simulated annealing, integer linear programming and genetic algorithm. In this paper we propose a adapted genetic algorithm searching solution space for the placement problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of each implementation. As a result, it was found that the adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.164-170
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• 2021

In VLSI design, partitioning is a task of clustering objects into groups so that a given objective circuit is optimized. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for partitioning include the Kernighan-Lin algorithm, Fiduccia-Mattheyses heuristic and simulated annealing. In this paper, we propose a adapted genetic algorithm searching solution space for the circuit partitioning problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of implementation. As a result, it was found that an adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.2
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• pp.196-206
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability, compared with mechanically scanned array radar. AESA radar brings a new challenges, radar resource management(RRM), which is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed stochastic radar beam scheduling algorithm using simulated annealing(SA), and evaluated the performance on the multi-function radar scenario. As a result, we showed that our proposed algorithm is superior to previous dispatching rule based scheduling algorithm from the viewpoint of beam processing latency and the number of scheduled beams, with real time capability.

• Structural Engineering and Mechanics
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• v.35 no.1
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• pp.19-35
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• 2010

This paper proposes a hybrid heuristic and criteria-based method of optimum design which combines the advantages of both the iterated simulated annealing (SA) algorithm and the rigorously derived optimality criteria (OC) for structural optimum design of reinforced concrete (RC) buildings under multi-load cases based on the current Chinese design codes. The entire optimum design procedure is divided into two parts: strength optimum design and stiffness optimum design. A modified SA with the strategy of adaptive feasible region is proposed to perform the discrete optimization of RC frame structures under the strength constraints. The optimum stiffness design is conducted using OC method with the optimum results of strength optimum design as the lower bounds of member size. The proposed method is integrated into the commercial software packages for building structural design, SATWE, and for finite element analysis, ANSYS, for practical applications. Finally, two practical frame-shear-wall structures (15-story and 30-story) are optimized to illustrate the effectiveness and practicality of the proposed optimum design method.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.107-110
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• 2003

In this paper, the optimal position selection of error sensors and transducers to attenuate interior noise from outside noise sources using active control techniques is presented. To get an optimal control characteristics in adaptive noise control systems, it is necessary to optimize the positions of sensors and transducers. A new type of simulated annealing method has been proposed as searching technique to find optimal transducers and sensors positions in which the sum of the squared pressures at sensor position in an enclosure can be best minimized. Computer simulations and experiments have been performed to show the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2766-2768
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• 2003

In this paper, positions of microphones and speakers in adaptive noise control systems are selected using optimization techniques. To get the optimal control characteristics of adaptive noise control system, It is necessary to optimize positions of microphones and speakers. Assume that control speakers are placed in a experimental enclosure several place indoors. Acoustic transfer functions from control speaker positions to microphone positions are measured for simulation program by experiments, and simulated annealing methods are used to select optimal positions of speakers and microphones.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.339-345
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• 2012

This research aims to formulate a mathematical model and develop an algorithm for solving a location problem in emergency medical service vehicle parking. To find an optimal parking location which has the least risk score or risk priority number calculated from severity, occurrence, detection, and distance from parking location for emergency patients, data were collected from Pratoom sub-district Disaster Prevention and Mitigation Center from October 2010 to April 2011. The criteria of risk evaluation were modified from Automotive Industry Action Group's criteria. An adaptive simulated annealing algorithm with multiple cooling schedules called multi-agent simulated quenching (MASQ) is proposed for solving the problem in two schemes of algorithms including dual agent and triple agent quenching. The result showed that the solution obtained from both scheme of MASQ was better than the traditional solution. The best locations obtained from MASQ-dual agent quenching scheme was nodes #5 and #133. The risk score was reduced 61% from 6,022 to 2,371 points.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.90-97
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• 2004

In this paper, the optimal position selection of error microphones and control speakers to attenuate interior noise from outside noise sources using active noise control techniques is presented. To get an optimal control characteristics of adaptive noise control systems, it is necessary to optimize the positions of microphones and speakers. New type of simulated annealing method has been proposed as searching techniques to find optimal speakers and microphones positions in which the sum of the squared pressures at microphone positions in an enclosure can be best minimized. Computer simulations and experiments have been performed to show the effectiveness of the proposed method.