• Journal of the Korean Mathematical Society
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• v.52 no.4
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• pp.797-819
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• 2015

A trapdoor discrete logarithm group is a cryptographic primitive with many applications, and an algorithm that allows discrete logarithm problems to be solved faster using a pre-computed table increases the practicality of using this primitive. Currently, the distinguished point method and one extension to this algorithm are the only pre-computation aided discrete logarithm problem solving algorithms appearing in the related literature. This work investigates the possibility of adopting other pre-computation matrix structures that were originally designed for used with cryptanalytic time memory tradeoff algorithms to work as pre-computation aided discrete logarithm problem solving algorithms. We find that the classical Hellman matrix structure leads to an algorithm that has performance advantages over the two existing algorithms.

• 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.

• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.259-268
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• 1999

In order to reduce the cost of product and save the processing time, optimal nesting of two-dimensional part is an important application in number of industries like shipbuilding and garment making. There have been many studies on finding the optimal solution of two-dimensional nesting. The problem of two-dimensional nesting has a non-deterministic characteristic and there have been various attempts to solve the problem by reducing the size of problem rather than solving the problem as a whole. Heuristic method and linearlization are often used to find an optimal solution of the problem. In this paper, theoretical and practical nesting algorithm for rectangular, circular and irregular shape of two-dimensional parts is proposed. Both No-Fit-Polygon and Minkowski-Sum are used for solving the overlapping problem of two parts and the dynamic programming technique is used for reducing the number search spae in order to find an optimal solution. Also, nesting designer's expertise is complied into the proposed algorithm to supplement the heuristic method.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.198-206
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• 2013

The team orienteering problem (TOP) or the multiple tour maximum collection problem can be considered as a generic model that can be applied to a number of challenging applications in logistics, tourism, and other fields. This problem is generally defined as the problem of determining P paths, in which the traveling time of each path is limited by $T_{max}$ that maximizes the total collected score. In the TOP, a set of N vertices i is given, each with a score $S_i$. The starting point (vertex 1) and the end point (vertex N) of all paths are fixed. The time $t_{ij}$ needed to travel from vertex i to j is known for all vertices. Some exact and heuristics approaches had been proposed in the past for solving the TOP. This paper proposes a new solution methodology for solving the TOP using the particle swarm optimization, especially by proposing a solution representation and its decoding method. The performance of the proposed algorithm is then evaluated using several benchmark datasets for the TOP. The computational results show that the proposed algorithm using specific settings is capable of finding good solution for the corresponding TOP instance.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.43-56
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• 2009

In korean peninsular, aircraft defense with SAM (Surface-to-Air Missile) is very important because of short range of combat space in depth. Effective and successful defense operation largely depends on two factors, SAM's location and the number of SAM for each target based on missile's availability in each SAM's location. However, most previous papers have handled only the former. In this paper, we developed Set covering model which can handle both factors simultaneously and Multi-heuristic algorithm for solving allocation problem of the batteries and missile assignment problem in each battery. Genetic algorithm is used to decide optimal location of the batteries. To determine the number of SAM, a heuristic algorithm is applied for solving missile assignment problem. If the proposed model is applied to allocation of SAM, it will improve the effectiveness of air defense operations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.137-142
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• 1996

An algorithm for solving the cardinality constrained linear programming knapsack problem is presented. The algorithm has a convenient structure for a branch-and-bound approach to the integer version, especially to the 0-1 collapsing knapsack problem. A numerical example is given.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.180-187
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• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

• Journal of The Korean Association of Information Education
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• v.20 no.2
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• pp.151-160
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• 2016

Algorithm education is emphasized as an instrument for teaching the basic principles of Computer Science. But these materials is very short-fall. We'll present the CS Unplugged-based algorithm contents, which is easy to learn for elementary student. These contents for self-directed learning consisted of the activity-based learning. For problem-solving algorithm learning in everyday life we were developed the hashing techniques on the basis of the basic searching and sorting algorithms. For checking the adequacy of these materials were tested by surveys of teacher professional groups, and we obtain the appropriate conclusions for sorting algorithm contents for improving the problem-solving ability for in elementary student.

• Management Science and Financial Engineering
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• v.4 no.2
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• pp.1-13
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• 1998

The Shortest Path Problem in Time-dependent Networks, where the travel time of each link depends on the time interval, is not realistic since the model and its solution violate the Non-passing Property (NPP:often referred to as FIFO) of real phenomena. Furthermore, solving the problem needs much more computational and memory complexity than the general shortest path problem. A new model for Time-dependent Networks where the flow speeds of each link depend on time interval, is suggested. The model is more realistic since its solution maintains the NPP. Solving the problem needs just a little more computational complexity, and the same memory complexity, as the general shortest path problem. A solution algorithm modified from Dijkstra's label setting algorithm is presented. We extend this model to the problem of Minimum Expected Time Path in Time-dependent Stochastic Networks where flow speeds of each link change statistically on each time interval. A solution method using the Kth-shortest Path algorithm is presented.

• Bulletin of the Korean Mathematical Society
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• v.47 no.4
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• pp.675-685
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• 2010

In this paper, we consider a new class of regularized (nonconvex) generalized mixed variational inequality problems in real Hilbert space. We give the concepts of partially relaxed strongly mixed monotone and partially relaxed strongly $\theta$-pseudomonotone mappings, which are extension of the concepts given by Xia and Ding [19], Noor [13] and Kazmi et al. [9]. Further we use the auxiliary principle technique to suggest a two-step iterative algorithm for solving regularized (nonconvex) generalized mixed variational inequality problem. We prove that the convergence of the iterative algorithm requires only the continuity, partially relaxed strongly mixed monotonicity and partially relaxed strongly $\theta$-pseudomonotonicity. The theorems presented in this paper represent improvement and generalization of the previously known results for solving equilibrium problems and variational inequality problems involving the nonconvex (convex) sets, see for example Noor [13], Pang et al. [14], and Xia and Ding [19].