• The KIPS Transactions:PartA
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• v.18A no.3
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• pp.115-122
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• 2011

It is very hard to obtain a general algorithm for solution of all the constraint satisfaction optimization problems. However, if the whole problem is separated into subproblems by characteristics of decision variables, we can assume that an algorithm to obtain solutions of these subproblems is easier. Under the assumption, we propose a problem classifying rule which subdivide the whole problem, and develop backtracking algorithms fit for these subproblems. One of the methods of finding a quick solution is efficiently arrange for any order of the search tree nodes. We choose the cluster head positioning problem in wireless sensor networks in which static characteristics is dominant and interference minimization problem of RFID readers that has hybrid mixture of static and dynamic characteristics. For these problems, we develop optimal variable ordering algorithms, and compare with the conventional methods. As a result of classifying the problem into subproblems, we can realize a backtracking framework for systematic search. We also have shown that developed backtracking algorithms have good performance in their quality.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.159-167
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• 2023

Objective: • To detect black hole and warm hole attacks in wireless sensor networks. • To give a solution for energy depletion and security breach in wireless sensor networks. • To address the security problem using strategic decision support system. Methods: The proposed stackelberg game is used to make the spirited relations between multi leaders and multi followers. In this game, all cluster heads are acts as leaders, whereas agent nodes are acts as followers. The game is initially modeled as Quadratic Programming and also use backtracking search optimization algorithm for getting threshold value to determine the optimal strategies of both defender and attacker. Findings: To find optimal payoffs of multi leaders and multi followers are based on their utility functions. The attacks are easily detected based on some defined rules and optimum results of the game. Finally, the simulations are executed in matlab and the impacts of detection of black hole and warm hole attacks are also presented in this paper. Novelty: The novelty of this study is to considering the stackelberg game with backtracking search optimization algorithm (BSOA). BSOA is based on iterative process which tries to minimize the objective function. Thus we obtain the better optimization results than the earlier approaches.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.51-57
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• 2008

We present a modeling of constraint satisfaction problems and provide heuristic algorithms of backtracking search to optimize the frequency assignment. Our research objective is to find a frequency assignment that satisfies all the constraints using minimum number of frequencies while maximizing the number of community radio stations served for a given area. In order to get a effective solution, some ordering heuristics such as variable orderings and value orderings are provided to minimize the backtracking in finding all solutions within a limited time. To complement the late detection of inconsistency in the backtracking, we provide the consistency enforcing technique or constraint propagation to eliminate the values that are inconsistent with some constraints. By integrating backtracking search algorithms with consistency enforcing techniques, it is possible to obtain more powerful and effective algorithms of constraint satisfaction problems. We also provide the performance evaluation of proposed algorithms by comparing the theoretical lower bound and our computed solution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.105-114
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• 2000

Job Shop Problem which consists of the m different machines and n jobs is a NP-hard problem of the combinatorial optimization. Each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. Each machine can process at most one operation at a time. The purpose of this paper is to develop the heuristic method to solve large scale scheduling problem using Constraint Satisfaction Problem method and Simulated Annealing. The proposed heuristic method consists of the search algorithm and optimization algorithm. The search algorithm is to find the solution in the solution space using CSP concept such as backtracking and domain reduction. The optimization algorithm is to search the optimal solution using SA. This method is applied to MT06, MT10 and MT20 Job Shop Problem, and compared with other heuristic method.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.239-246
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• 2009

This paper deals with the makespan minimization problem of job shops. The problem is known as one of hard problems to optimize, and therefore, many heuristic methods have been proposed by many researchers. The aim of this study is also to propose a heuristic scheduling method for the problem. However, the difference between the proposed method and many other heuristics is that the proposed method is based on depth-first branch and bound, and thus it is possible to find an optimal solution at least in principle. To accelerate the search, when a node is judged hopeless in the search tree, the proposed flexible branch and bound method can indicate a higher backtracking node. The unexplored nodes are stored and may be explored later to realize the strict optimization. Two methods are proposed to generate the backtracking point based on the critical path of the current best feasible schedule, and the minimum lower bound for the makespan in the unexplored sub-problems. Schedules are generated based on Giffler and Thompson's active schedule generation algorithm. Acceleration of the search by the flexible branch and bound is confirmed by numerical experiment.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.131-136
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• 1990

In this paper, new methods are proposed which speed up the logical redundancy identification for the gate-level logic optimization. Redundancy indentification, as well as deterministic test pattern generation, can be viewed as a finite space search problem, of which execution time depends on the size of the search space. For the purpose of efficient search, we propose dynamic head line and mandatory assignment. Dynamic head lines are changed dynamically in the process of the redundancy identification. Mandatory assignement can avoid unnecessary assignment. They can reduce the search size efficiently. Especially they can be used even though the circuit is modified in the optimization procedure, that is different from the test pattern generation methods. Some experimental results are presented indicating that the proposed methods are faster than existing methods.

• Journal of applied mathematics & informatics
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• v.29 no.1_2
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• pp.173-190
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• 2011

In this paper, we construct a new approach of affine scaling interior algorithm using the affine scaling conjugate gradient and Lanczos methods for bound constrained nonlinear optimization. We get the iterative direction by solving quadratic model via affine scaling conjugate gradient and Lanczos methods. By using the line search backtracking technique, we will find an acceptable trial step length along this direction which makes the iterate point strictly feasible and the objective function nonmonotonically decreasing. Global convergence and local superlinear convergence rate of the proposed algorithm are established under some reasonable conditions. Finally, we present some numerical results to illustrate the effectiveness of the proposed algorithm.