• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.236-244
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• 2012

The minimum energy broadcast problem is for all deployed nodes to minimize a total transmission energy for performing a broadcast operation in wireless networks. In this paper, we propose a Tabu search algorithm to solve efficiently the minimum energy broadcast problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the total transmission energy of nodes and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the minimum energy broadcast problem in wireless sensor networks.

• 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.

• Journal of Information Processing Systems
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• v.5 no.4
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• pp.237-242
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• 2009

The classical vehicle routing problem (VRP) can be extended by including customers who want to send goods to the depot. This type of VRP is called the vehicle routing problem with pickups and deliveries (VRPPD). This study proposes a novel way to solve VRPPD by introducing a two-phase heuristic routing algorithm which consists of a clustering phase and uses the geometrical center of a cluster and route establishment phase by applying a two-way search of each route after applying the TSP algorithm on each route. Experimental results show that the suggested algorithm can generate better initial solutions for more computer-intensive meta-heuristics than other existing methods such as the giant-tour-based partitioning method or the insertion-based method.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• Structural Engineering and Mechanics
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• v.62 no.6
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• pp.749-758
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• 2017

In structural mechanics, traditional analyses methods usually employ matrix operations for obtaining displacement and internal forces of the structure under the external effects, such as distributed loads, earthquake or wind excitations, and temperature changing inter alia. These matrices are derived from the well-known principle of mechanics called minimum potential energy. According to this principle, a system can be in the equilibrium state only in case when the total potential energy of system is minimum. A close examination of the expression of the well-known equilibrium condition for linear problems, $P=K{\Delta}$, where P is the load vector, K is the stiffness matrix and ${\Delta}$ is the displacement vector, it is seen that, basically this principle searches the displacement set (or deformed shape) for a system that minimizes the total potential energy of it. Instead of using mathematical operations used in the conventional methods, with a different formulation, meta-heuristic algorithms can also be used for solving this minimization problem by defining total potential energy as objective function and displacements as design variables. Based on this idea the technique called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) is proposed. The method has been successfully applied for linear and non-linear analyses of trusses and truss-like structures, and the results have shown that the approach is much more successful than conventional methods, especially for analyses of non-linear systems. In this study, the application of TPO/MA, with Harmony Search as the selected meta-heuristic algorithm, to cables net system is presented. The results have shown that the method is robust, powerful and accurate.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.58-65
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• 2021

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1417-1426
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• 2017

Power system analysis, Non-Convex Economic Dispatch (NED) is considered as an open and demanding optimization problem. Despite the fact that realistic ED problems have non-convex cost functions with equality and inequality constraints, conventional search methods have not been able to effectively find the global answers. Considering the great potential of meta-heuristic optimization techniques, many researchers have started applying these techniques in order to solve NED problems. In this paper, a new and efficient approach is proposed based on imperialist competitive algorithm (ICA). The proposed algorithm which is named multi-operator ICA (MuICA) merges three operators with the original ICA in order to simultaneously avoid the premature convergence and achieve the global optimum answer. In this study, the proposed algorithm has been applied to different test systems and the results have been compared with other optimization methods, tending to study the performance of the MuICA. Simulation results are the confirmation of superior performance of MuICA in solving NED problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.9-12
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• 2000

컨테이너 터미널에서 효율적인 적하작업 계획을 자동으로 생성하는 알고리즘을 연구하였다. 실제 터미널에서 계획자들이 적하작업 계획시에 고려하는 제약소건 및 효율적인 계획을 위한 고려사항을 조사하였다. 이를 바탕으로 1단계에서는 개미시스템(ant system)이라는 인공지능기법을 적용하여 제약조건을 만족시키면서 원활한 적하작업이 진행될 수 있도록 컨테이너 크레인과 트랜스퍼 크레인의 이동순서와 위치를 결정하고, 2단계에서는 1단계에서의 결과를 바탕으로 빔탐색법(beam search)을 사용하여 컨테이너 개개의 작업순서를 결정하는 알고리즘을 개발하였다. 또한 개발된 시스템의 성능을 검증하기 위하여 최근의 대형선반에 대한 실제 현장자료를 바탕으로 실험을 수행하였다.

• Steel and Composite Structures
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• v.16 no.6
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• pp.681-701
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• 2014

The main aim of this study is to propose an efficient meta-heuristic algorithm for topology optimization of geometrically nonlinear single layer domes by serially integration of computational advantages of firefly algorithm (FA) and particle swarm optimization (PSO). During the optimization process, the optimum number of rings, the optimum height of crown and tubular section of the member groups are determined considering geometric nonlinear behaviour of the domes. In the proposed algorithm, termed as FA-PSO, in the first stage an optimization process is accomplished using FA to explore the design space then, in the second stage, a local search is performed using PSO around the best solution found by FA. The optimum designs obtained by the proposed algorithm are compared with those reported in the literature and it is demonstrated that the FA-PSO converges to better solutions spending less computational cost emphasizing on the efficiency of the proposed algorithm.

• Smart Structures and Systems
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• v.20 no.5
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• pp.607-618
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• 2017

In this research, a newly developed nature-inspired optimization method, the Lion Pride Optimization algorithm (LPOA), is utilized for optimal design of composite steel box girder bridges. A composite box girder bridge is one of the common types of bridges used for medium spans due to their economic, aesthetic, and structural benefits. The aim of the present optimization procedure is to provide a feasible set of design variables in order to minimize the weight of the steel trapezoidal box girders. The solution space is delimited by different types of design constraints specified by the American Association of State Highway and Transportation Officials. Additionally, the optimal solution obtained by LPOA is compared to the results of other well-established meta-heuristic algorithms, namely Gray Wolf Optimization (GWO), Ant Lion Optimizer (ALO) and the results of former researches. By this comparison the capability of the LPOA in optimal design of composite steel box girder bridges is demonstrated.