• Korean Management Science Review
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• v.30 no.2
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• pp.31-42
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• 2013

The vehicle routing problem (VRP) can be described as a problem to find the optimum traveling routes from one or several depot (s) to number of geographically scattered customers. This study executes a revised Heterogeneous Vehicle Routing Problem (HVRP) to minimize the cost that needs to conduct efficiently the snow removal operations of Air Wing under available resources and limited operations time. For this HVRP, we model the algorithm of an hybrid Ant Colony System (ACS). In the initial step for finding a solution, the modeled algorithm applies various alterations of a parameter that presents an amount of pheromone coming out from ants. This improvement of the initial solution illustrates to affect to derive better result ultimately. The purpose of this study proves that the algorithm using Hybrid heuristic incorporated in tabu and ACS develops the early studies to search best solution.

• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.405-426
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• 2014

In this paper, a new meta-heuristic algorithm named Ranked Particles Optimization (RPO), is presented. This algorithm is not inspired from natural or physical phenomena. However, it is based on numerous researches in the field of meta-heuristic optimization algorithms. In this algorithm, like other meta-heuristic algorithms, optimization process starts with by producing a population of random solutions, Particles, located in the feasible search space. In the next step, cost functions corresponding to all random particles are evaluated and some of those having minimum cost functions are stored. These particles are ranked and their weighted average is calculated and named Ranked Center. New solutions are produced by moving each particle along its previous motion, the ranked center, and the best particle found thus far. The robustness of this algorithm is verified by solving some mathematical and structural optimization problems. Simplicity of implementation and reaching to desired solution are two main characteristics of this algorithm.

• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.491-497
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• 2007

The planning and scheduling is considered as an important function because it influence to the overall performance of production in manufacturing system for supporting global objectives. In this paper, propose the hybrid approach to solve the problem which considered alternative resources and sequences, a sequence-dependent setups and transportation times. The objective of this problem is minimize the makespan with AGV traveling time. A iterative two heuristic algorithm(PSO, SA) is proposed to obtain the sequence of operations and CPLEX package is used to obtain the optimal sub-lot sizes(OLS) when the sequence of operations and greedy-based AGV dispatching rules are fixed or given. we show that performance of iterative two heuristic algorithms and simple numerical experiment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.27-37
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• 2005

The rectilinear Steiner tree problem (RSTP) is to find a minimum-length rectilinear interconnection of a set of terminals in the plane. It is well known that the solution to this problem will be the minimal spanning tree (MST) on some set Steiner points. The RSTP is known to be NP-complete. The RSTP has received a lot of attention in the literature and heuristic and optimal algorithms have been proposed, A key performance measure of the algorithm for the RSTP is the reduction rate that is achieved by the difference between the objective value of the RSTP and that of the MST without Steiner points. A hybrid evolution strategy on RSTP based upon the Prim algorithm was presented. The computational results show that the evolution strategy is better than the previously proposed other heuristic. The average reduction rate of solutions from the evolution strategy is about 11%, which is almost similar to that of optimal solutions.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.184-194
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• 2014

Numerous studies have been studied to provide an efficient solution for the Nurse Rostering Problem (NRP), most of which have suffered from its complexity arising from incorporating nurse's work shift and ability. The test-bed data for the NRP is released for the public Competition in 2010. This study suggests a new mixed integer programming for Nurse Rostering Problem and develops a hybrid approach, where a hierarchical decomposition and the corresponding optimization are combined. The computation experiment is performed to show that the suggested algorithms may give a better solution in various instances, compared to the one appeared in the literature.

• Advances in Energy Research
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• v.7 no.1
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• pp.67-84
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• 2020

Given the recent surge of interest towards utilization of renewable distributed energy resources (DER), in particular in remote areas, this paper aims at designing an optimal hybrid system in order to supply loads of a village located in Esfarayen, North Khorasan, Iran. This paper illustrates the optimal design procedure of a standalone hybrid system which consists of Wind Turbine Generator (WTG), Photo Voltaic (PV), Diesel-generator, and Battery denoting as the Energy Storage System (ESS). The WTGs and PVs are considered as the main producers since the site's ambient conditions are suitable for such producers. Moreover, batteries are employed to smooth out the variable outputs of these renewable resources. To this end, whenever the available power generation is higher than the demanded amount, the excess energy will be stored in ESS to be injected into the system in the time of insufficient power generation. Since the standalone system is assumed to have no connection to the upstream network, it must be able to supply the loads without any load curtailment. In this regard, a Diesel-Generator can also be integrated to achieve zero loss of load. The optimal hybrid system design problem is a discrete optimization problem that is solved, here, by means of a recently-introduced meta-heuristic optimization algorithm known as Lightning Attachment Procedure Optimization (LAPO). The results are compared to those of some other methods and discussed in detail. The results also show that the total cost of the designed stand-alone system in 25 years is around 92M€ which is much less than the grid-connected system with the total cost of 205M€. In summary, the obtained simulation results demonstrate the effectiveness of the utilized optimization algorithm in finding the best results, and the designed hybrid system in serving the remote loads.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.179-184
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• 2004

This paper presents an image segmentation algorithm to obtain the 3D body shape data that the grid pattern and the body contour lute in the background image are extracted using the new proposed hybrid method. The body contour line is extracted based on maximum biased anisotropic recognition(MaxBAR) algorithm which recognizes the most strong and robust edges in the image since the normal derivative at the edges is large, while the tangential derivatives can be small. The grid patterns within body contour lines are extracted by grid pattern detection (GPD). The body contour lilies and the grid patterns are combined. The consecutive run test based on heuristic method is used to link the disconnected line and reduce noise line. This proposed segmentation method is more effective than the conventional method which uses a gradient and a laplacian operator, verified with application two conventional method.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.37-48
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• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.45-54
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• 2014

In this paper, an improved two-step P2P traffic classification scheme is proposed to overcome the limitations of the existing methods. The first step is a signature-based classifier at the packet-level. The second step consists of pattern heuristic rules and a statistics-based classifier at the flow-level. With pattern heuristic rules, the accuracy can be improved and the amount of traffic to be classified by statistics-based classifier can be reduced. Based on the analysis of different decision tree algorithms, the statistics-based classifier is implemented with REPTree. In addition, the ensemble algorithm is used to improve the performance of statistics-based classifier Through the verification with the real datasets, it is shown that our hybrid scheme provides higher accuracy and lower overhead compared to other existing schemes.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.