• Journal of the Korea Safety Management & Science
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• v.13 no.3
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• pp.107-114
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• 2011

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.17-24
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• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1667-1673
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• 2009

This paper presents a application of new algorithm for feeder reconfiguration problem in distribution systems. Harmony Search (HS) algorithm, which is motivated from the musical performance, is used to reconfigure distribution systems so that active power losses are globally minimized with turning on/off the sectionalizing and the tie-line switches. In optimization processing, the HS algorithm has searching ability for the global optimal solution, simple coding of the iteration procedure, and fast convergence to get the solution. The HS algorithm is tested on 15 buses and 69 buses distribution systems, and the results prove its effectiveness to determine appropriate switching options without the occurrence of any misdetermination in switching and get the minimum power loss.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.19-27
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• 2009

This paper presents a newly developed optimization algorithm, the Bee Optimization Algorithm (BeeOA), to solve the economic power dispatch (EPD) problem. The authors have developed a derivative free and global optimization technique based on the working of the honey bee. The economic power dispatch problem is a nonlinear constrained optimization problem. Classical optimization techniques fail to provide a global solution and evolutionary algorithms provide only a good enough solution. The proposed approach has been examined and tested on two test systems with different objectives. A simple power dispatch problem is tested first on 6 generators and then the algorithm is demonstrated on 13 thermal unit systems whose incremental fuel cost function takes into account the value point loading effect. The results are promising and show the effectiveness and robustness of the proposed approach over recently reported methods.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.8
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• pp.399-410
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• 2005

This paper Proposes a novel adaptive genetic algorithm (GA) extrapolated by an ant colony optimization. We first prove that the algorithm converges to the unique global optimal solution with probability arbitrarily close to one and then, by experimental studies, show that the algorithm converges faster to the optimal solution than GA with elitism and the population average fitness value also converges to the optimal fitness value. We further discuss controlling the tradeoff of exploration and exploitation by a parameter associated with the proposed algorithm.

• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.61-67
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• 2008

To design flight control law of an unmanned aerial vehicle, automated control gain determination program was developed. The procedure for determination of control gain was formulated as the control gains were designed from the optimal solutions of the optimization problem. PSO algorithm, which is one of the evolutionary computation method, and SQP algorithm, which is one of the nonlinear programming method, are used as optimization problem solver. Thru this technique, computation time required for finding the optimal solution is decreased to 1/5 of that of PSO algorithm and more accurate optimal solution is obtained.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.1
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• pp.57-70
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• 2004

Given a tree network on which each node has its own demand and also stands for a candidate location of a potential facility. such as plant or warehouse, the f-median problem on the network (PMPOT) is to select less than or equal to P number of facility locations so that the whole demand on a node is satisfied from only one facility and the total demand occurred on the network can be satisfied from those facilities with the minimum total cost, where the total cost Is the sum of transportation costs and the fixed costs of establishing facilities. Tamir(1996) developed an O(p n$^2$) algorithm for PMPOT which is known to be the best algorithm In terms of the time complexity, where n is the number of nodes in the network, but he didn't make any comments or explanation about implementation details for finding the optimal solution. In contrast to Tamir's work, Kariv and Hakimi(1979) developed O(p$^2$n$^2$) algorithm for PMPOT and presented O(n$^2$) algorithm for finding the optimal solution in detail. In this paper, we not only develop another O(p n$^2$) dynamic programming algorithm for PMPOT that is competitive to Tamir's algorithm in terms of the time complexity, but also present O(n) algorithm that is more efficient than kariv and Hakimi's algorithm in finding the optimal solution. finally, we implement our algorithm on a set of randomly generated problems and report the computational results.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.889-898
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• 2013

The shortest-path searching algorithm must not only find a global solution to the destination, but also solve a turn penalty problem (TPP) in an urban road transportation network (URTN). Although the Dijkstra algorithm (DA) as a representative node-based algorithm secures a global solution to the shortest path search (SPS) in the URTN by visiting all the possible paths to the destination, the DA does not solve the TPP and the slow execution speed problem (SEP) because it must search for the temporary minimum cost node. Potts and Oliver solved the TPP by modifying the visiting unit from a node to the link type of a tree-building algorithm like the DA. The Multi Tree Building Algorithm (MTBA), classified as a representative Link Based Algorithm (LBA), does not extricate the SEP because the MTBA must search many of the origin and destination links as well as the candidate links in order to find the SPS. In this paper, we propose a new Link-Based Single Tree Building Algorithm in order to reduce the SEP of the MTBA by applying the breaking rule to the LBA and also prove its usefulness by comparing the proposed with other algorithms such as the node-based DA and the link-based MTBA for the error rates and execution speeds.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.1
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• pp.27-35
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• 1989

An algorithm that searches the general AND-OR graph is proposed. The convergence and the efficiency of the algorithm is examined and compared with an existing algorithm for the AND-OR graph. It is proved that the proposed algorithm is superior to the existing method both in the quality of the solution and the number of node expansions.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.14-24
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• 2002

We focus on resource allocation planning in container terminal operation planning problems and present network design model and genetic algorithm. We present a network design model in which arc capacities must be properly dimensioned to sustain the container traffic. This model supports various planning aspects of container terminal and brings in a very general form. The integer programming model of network design can be extended to accommodate vertical or horizontal yard configuration by adding constraints such as restricting the sum of yard cranes allocated to a block of yards. We devise a genetic algorithm for the network design model in which genes have the form of general integers instead of binary integers. In computational experiments, it is found that the genetic algorithm can produce very good solution compared to the optimal solution obtained by CPLEX in terms of computation time and solution quality. This algorithm can be used to generate many alternatives of a resource allocation plan for the container terminal and to evaluate the alternatives using various tools such as simulation.