• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.193-198
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• 2022

Arthur Miller's play has been reproduced for a long time, and has been made into a film several times. Director Asghar Farhadi made a film set in Iran in the 21st century, showing the film (2017), which excludes "Death" from the original title. is not just a movie of . In , the play is summoned in the form of performing a play. There are many movies in this form, but is an exquisite fabrication so that the reality outside the play and the content in the play harmonize with each other. The play depicts the tragedy of the head of the family who falls at the end of the American dream. The movie transforms this tragedy into a conflict between a young couple living in Iran in the 21st century. In addition, is completed as an independent work that not only rearranged the space and characters of the original work, but also reinterpreted the meaning of death, creating the effect of media conversion such as theater and film.

• 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 the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.17-26
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• 2008

The traveling salesman problem is to find tours through all cities at minimum cost ; simply visiting the cities only once that a salesman wants to visit. As such, the traveling salesman problem is a NP-complete problem ; an heuristic algorithm is preferred to an exact algorithm. In this paper, we suggest an effective cost relaxation using a candidate arc set which is obtained from a regression function for the traveling salesman problem. The proposed method sufficiently consider the characteristics of cost of arcs compared to existing methods that randomly choose the arcs for relaxation. For test beds, we used 31 instances over 100 cities existing from TSPLIB and randomly generated 100 instances from well-known instance generators. For almost every instances, the proposed method has found efficiently better solutions than the existing method.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.636-643
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• 2020

The differential evolution algorithm is one of the meta-heuristic techniques developed to solve the real optimization problem, which is a continuous problem space. In this study, in order to use the differential evolution algorithm to solve the traveling salesman problem, which is a discontinuous problem space, a random key representation method is applied to the differential evolution algorithm. The differential evolution algorithm searches for a real space and uses the order of the indexes of the solutions sorted in ascending order as the order of city visits to find the fitness. As a result of experimentation by applying it to the benchmark traveling salesman problems which are provided in TSPLIB, it was confirmed that the proposed differential evolution algorithm based on the random key representation method has the potential to solve the traveling salesman problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.105-111
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• 2004

DNA computing has been using to solve TSP (Traveling Salesman Problems). However, when the typical DNA computing is applied to TSP, it can`t efficiently express vertices and weights of between vertices. In this paper, we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to efficiently express vertices and weights of between vertices for TSP. We applied ACO to TSP and as a result ACO could express DNA codes which have variable lengths and weights of between vertices more efficiently than Adleman`s DNA computing algorithm could. In addition, compared to Adleman`s DNA computing algorithm, ACO could reduce search time and biological error rate by 50% and could search for a shortest path in a short time.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.222-227
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• 2006

Recently the research for Traveling Salesman Problem (TSP) using DNA computing with massive parallelism has been. However, there were difficulties in real biological experiments because the conventional method didn't reflect the precise characteristics of DNA when it express graph. Therefore, we need DNA sequence generation algorithm which can reflect DNA features and reduce biological experiment error. In this paper we proposed a DNA sequence generation algorithm that applied DNA coding method of evolution model to DNA computing. The algorithm was applied to TSP, and compared with a simple genetic algorithm. As a result, the algorithm could generate good sequences which minimize error and reduce the biologic experiment error rate.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.55-64
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• 2023

The travelling salesman problem (TSP) is an important combinatorial optimization problem that is used in several engineering science branches and has drawn interest to several researchers and scientists. In this problem, a salesman from an arbitrary node, called the warehouse, starts moving and returns to the warehouse after visiting n clients, given that each client is visited only once. The objective in this problem is to find the route with the least cost to the salesman. In this study, a meta-based ant colony system algorithm (ACSA) is suggested to find solution to the TSP that does not use local pheromone update. This algorithm uses the global pheromone update and new heuristic information. Further, pheromone evaporation coefficients are used in search space of the problem as diversification. This modification allows the algorithm to escape local optimization points as much as possible. In addition, 3-opt local search is used as an intensification mechanism for more quality. The effectiveness of the suggested algorithm is assessed on a several standard problem instances. The results show the power of the suggested algorithm which could find quality solutions with a small gap, between obtained solution and optimal solution, of 1%. Additionally, the results in contrast with other algorithms show the appropriate quality of competitiveness of our proposed ACSA.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.193-201
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• 2023

In this paper, we consider the maximum scatter traveling salesman problem (MSTSP), a travelling salesman problem (TSP) variant. The problem aims to maximize the minimum length edge in a salesman's tour that travels each city only once in a network. It is a very complicated NP-hard problem, and hence, exact solutions can be found for small sized problems only. For large-sized problems, heuristic algorithms must be applied, and genetic algorithms (GAs) are found to be very successfully to deal with such problems. So, this paper develops a hybrid GA (HGA) for solving the problem. Our proposed HGA uses sequential sampling algorithm along with 2-opt search for initial population generation, sequential constructive crossover, adaptive mutation, randomly selected one of three local search approaches, and the partially mapped crossover along with swap mutation for perturbation procedure to find better quality solution to the MSTSP. Finally, the suggested HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes. Our computational experience reveals that the suggested HGA is better. Further, we provide solutions to some asymmetric TSPLIB instances of many sizes.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.48-51
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• 2000

The traveling salesman problem with precedence relations (TSPPR) is harder than general traveling salesman problem. In this paper we propose an efficient genetic algorithm (GA) to solve the TSPPR. The key concept of the proposed genetic algorithm is a topological sort (TS). The results of numerical experiments show that the proposed GA approach produces an optimal solution for the TSPPR.

• Journal of Korean Institute of Industrial Engineers
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• v.11 no.2
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• pp.155-163
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• 1985

This paper presents a heuristic algorithm for a multidepot aircraft scheduling and crew scheduling with deal-head flights. This algorithm is extended from a Greedy heuristic algorithm for a multi-depot multi-salesman traveling salesman problem. We first transform a given flight schedule into a multi-depot multi-traveling salesman problem, considering aircraft flight policies and crew management constraints. Then we solve this problem by applying a modified Greedy heuristic algorithm.