• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.9
• /
• pp.116-123
• /
• 1997

We have already presented the constraint satisfaction algorithm which could solve the closed loop porblem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and effi- ciently. This algorithm is a hybrid type of using both declarative description (constraint representation) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.1020-1025
• /
• 2005

The set covering(SC) problem has many practical application of modeling not only real world problems in civilian but also in military. In this paper we study optimal allocation model for maximizing utility of consolidating old fashioned and new air defense weapon system like Patriot missile and develop the new computational algorithm for the SC problem by using simulated annealing(SA) algorithm. This study examines three different methods: 1) simulated annealing(SA); 2) accelerated simulated annealing(ASA); and 3) selection by effectiveness degree(SED) with SA. The SED is adopted as an enhanced SA algorithm that the neighboring solutions could be generated only in possible optimal feasible region at the PERTURB function. Furthermore, we perform various experiments for both a reduced and an extended scale sized situations depending on the number of customers(protective objective), service(air defense), facilities(air defense artillery), threat, candidate locations, and azimuth angles of Patriot missile. Our experiment shows that the SED obtains the best results than others.

• IE interfaces
• /
• v.12 no.1
• /
• pp.94-101
• /
• 1999

The set covering(SC) problem is the problem of covering all the rows of an $m{\times}n$ matrix of ones and zeros by a subset of columns with a minimal cost. It has many practical applications of modeling of real world problems. The SC problem has been proven to be NP-complete and many algorithms have been presented to solve the SC problem. In this paper we present hybrid simulated annealing(HSA) algorithm based on the Simulated Annealing(SA) for the SC problem. The HSA is an algorithm which combines SA with a crossover operation in a genetic algorithm and a local search method. Our experimental results show that the HSA obtains better results than SA does.

• Journal of the Institute of Convergence Signal Processing
• /
• v.3 no.1
• /
• pp.87-92
• /
• 2002

Placement is an important step in the physical design of VLSI circuits. It is the problem of placing a set of circuit modules on a chip to optimize the circuit performance. The most popular algorithms for placement include the cluster growth, simulated annealing and integer linear programming. In this paper we propose a stochastic evolution algorithm searching solution space for the placement problem, and then compare it with simulated annealing by analyzing the results of each implementation.

• Proceedings of the KIEE Conference
• /
• 1998.11a
• /
• pp.335-337
• /
• 1998

This paper presents a efficient algorithm for loss reduction of distribution system by automatic sectionalizing switch operation in distribution systems of radial type. Simulated Annealing can avoid escape from local minima by accepting improvements in cost, but the use of this algorithm is also responsible for an excessive computation time requirement. To overcome this major limitation of Simulated Annealing Algorithm, we may use advanced Simulated Annealing Algorithm. Numerical examples demonstrate the validity and effectiveness of the proposed methodology.

• Journal of Information Processing Systems
• /
• v.18 no.4
• /
• pp.562-574
• /
• 2022

As traditional ways of evaluation prove to be ineffective in evaluating the effect of interactive multimedia physical education (PE) teaching, this study develops a new evaluation model based on the simulated annealing algorithm. After the evaluation subjects and the principle of the evaluation system are determined, different subjects are well chosen to constitute the evaluation system and given the weight. The backpropagation neural network has been improved through the simulated annealing algorithm, whose improvement indicates the completion of the evaluation model. Simulation results show that the evaluation model is highly efficient. Compared with traditional evaluation models, the proposed one enhances students' performance in PE classes by 50%.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.32A no.2
• /
• pp.111-119
• /
• 1995

In this paper, we propose an efficient SA(simulated annealing) algorithm for the synthesis of binary phase computer generated hologram. SA algorithm is a method to find the optimal solution through iterative technique. It is important that selecting cost function and parameters within this algorithm. The aplications of converentional SA algorithm to synthesize parameters within this algorithm. The applications of conventional SA algorithm to synthesize binary hologram have many problems because of inappropriate paramters and cost function. So, we propose a new cost function and a calculation technique of proper parameters required to achieve the optimal solution. Computer simulation results show that the proposed method is better than conventional method in terms of diffraction efficiency and reconstruction error. Also, we show the reconstructed images by the proposed method through optical esperiment.

• The Journal of Information Systems
• /
• v.28 no.4
• /
• pp.175-197
• /
• 2019

Purpose This study develops a heuristic algorithm to solve the time-resource tradeoffs in project scheduling problems with a real basis. Design/methodology/approach Resource constrained project scheduling problem with time-resource tradeoff is well-known as one of the NP-hard problems. Previous researchers have proposed heuristic that minimize Makespan of project scheduling by deriving optimal combinations from finite combinations of time and resource. We studied to solve project scheduling problems by deriving optimal values from infinite combinations. Findings We developed heuristic algorithm named Push Algorithm that derives optimal combinations from infinite combinations of time and resources. Developed heuristic algorithm based on simulated annealing shows better improved results than genetic algorithm and further research suggestion was discussed as a project scheduling problem with multiple resources of real numbers.

• ETRI Journal
• /
• v.15 no.3
• /
• pp.1-26
• /
• 1994

Simulated annealing is an attractive, but expensive, heuristic method for approximating the solution to combinatorial optimization problems. Attempts to parallel simulated annealing, particularly on distributed memory multicomputers, are hampered by the algorithm's requirement of a globally consistent system state. In a multicomputer, maintaining the global state S involves explicit message traffic and is a critical performance bottleneck. To mitigate this bottleneck, it becomes necessary to amortize the overhead of these state updates over as many parallel state changes as possible. By using this technique, errors in the actual cost C(S) of a particular state S will be introduced into the annealing process. This paper places analytically derived bounds on this error in order to assure convergence to the correct optimal result. The resulting parallel simulated annealing algorithm dynamically changes the frequency of global updates as a function of the annealing control parameter, i.e. temperature. Implementation results on an Intel iPSC/2 are reported.

• Journal of Power System Engineering
• /
• v.2 no.3
• /
• pp.60-67
• /
• 1998

Edge detection is the first step and very important step in image analysis. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for comparing the performances of different detectors. This cost function is made of desirable characteristics of edges such as thickness, continuity, length, region dissimilarity. And we use a simulated annealing algorithm for minimum of cost function. Simulated annealing are a class of adaptive search techniques that have been intensively studied in recent years. We present five strategies for generating candidate states. Experimental results(building image and test image) which verify the usefulness of our simulated annealing approach to edge detection are better than other operator.