• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.930-939
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• 2021

In recent years, the use of broadband bandwidth and redundant links for stable transmission in networks has resulted in excessive energy consumption and reduced transmission efficiency. In this paper, we propose an optimization algorithm that reduces the number of transmission links and minimizes transmission energy by removing redundant traffic in networks where traffic redundancy is allowed. The optimization algorithm proposed in this paper uses the meta-heuristic method using Tabu search algorithm. The proposed optimization algorithm minimizes transmission energy by designing a neighborhood generation method that efficiently routes overlapping traffic. The performance evaluation of the proposed optimization algorithm was performed in terms of the number of links used to transmit all traffic generated in the network and the transmission energy consumed. From the performance evaluation results, it was confirmed that the proposed algorithm is superior to other algorithms previously proposed.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.38-45
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• 2003

This paper presents a meta-heuristic solution technique, Ant System (AS)algerian to solve edge detection problem. We define the quality of edge in terms of dissimilarity, continuity, thickness and length. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that inversely evaluates the quality of edge configuration. Twelve windows for enhancing dissimilarity regions based on the valid edge structures are used. The AS algorithm finds the optimal set of edge pixels based on the cost function. The experimental results show that the properly reduced set of edge pixels could be found regardless how complicated the image is.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.597-609
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• 2023

One of the applicable methods for the stabilization of soil walls is the nailing system which consists of tensile struts. The stability and safety of soil nail wall systems are influenced by the geometrical parameters of the nailing system. Generally, the determination of nailing parameters in order to achieve optimal performance of the nailing system for the safety of soil walls is defined in the framework of optimization problems. Also, according to the various uncertainty in the mechanical parameters of soil structures, it is necessary to evaluate the reliability of the system as a probabilistic problem. In this paper, the optimal design of the nailing system is carried out in deterministic and probabilistic cases using meta-heuristic and reliability-based design optimization methods. The colliding body optimization algorithm and first-order reliability method are used for optimization and reliability analysis problems, respectively. The objective function is defined based on the total cost of nails and safety factors and reliability index are selected as constraints. The mechanical properties of the nailing system are selected as design variables and the mechanical properties of the soil are selected as random variables. The results show that the reliability of the optimally designed soil nail system is very sensitive to uncertainty in soil mechanical parameters. Also, the design results are affected by uncertainties in soil mechanical parameters due to the values of safety factors. Reliability-based design optimization results show that a nailing system can be designed for the expected level of reliability and failure probability.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.612-620
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• 2017

Relay coordination in power system is a complex problem and so far, meta-heuristic algorithms and other methods as an alternative approach may not properly deal with large scale relay coordination due to their huge time consuming computation. In some cases the relay coordination could be unachievable. As the urgency for a proper approach is essential, in this paper an innovative and simple relay coordination method is introduced that is able to be applied on optimization algorithms for relay protection coordination. The objective function equation of operating time of relays are divided into two separate functions with less constraints. As the analytical results show here, this equivalent method has a remarkable speed with high accuracy to coordinate directional relays. Two distribution systems including directional overcurrent relays are studied in DigSILENT software and the collected data are examined in MATLAB. The relay settings of this method are compared with particle swarm optimization and genetic algorithm. The analytical results show the correctness of this mathematical and practical approach. This fast coordination method has a proper velocity of convergence with low iteration that can be used in large scale systems in practice and also to provide a feasible solution for protection coordination in smart grids as online or offline protection coordination.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.572-580
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• 2021

In order to reduce transmission delay and increase reliability in networks, mainly high-performance and high-power network equipment is used to guarantee network quality. In this paper, we propose an optimization algorithm to minimize the energy consumed when transmitting traffic in networks with a bundle link composed of multiple physical cables. The proposed optimization algorithm is a meta-heuristic method, which uses tabu search algorithm. In addition, it is designed to minimize transmission energy by minimizing the cables on the paths of the source and destination nodes for each traffic. In the proposed optimization algorithm, performance evaluation was performed in terms of the number of cables used in the transmission and the link utilization for all traffic on networks, and the performance evaluation result confirmed the superior performance than the previously proposed method.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.443-448
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• 2013

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1193-1201
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• 2020

Cell planning, which determines the cell structure for location management of mobile terminals in mobile communications, has been dealt with as an important research task to determine network performance. Among the factors influencing the cell structure planning in mobile communication, the signal cost for location management plays the most important role. In this paper, we propose an optimization algorithm that minimizes the location management cost of all the cells used to plan the cell structure in the network with reporting cell structure in mobile communication. The proposed algorithm uses a Tabu search algorithm, which is a meta-heuristic algorithm, and the proposed algorithm proposes a new neighborhood generation method to obtain a result close to the optimal solution. In order to evaluate the performance of the proposed algorithm, the simulation was performed in terms of location management cost and algorithm execution time. The evaluation results show that the proposed algorithm outperforms the existing genetic algorithm and simulated annealing.