• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.75-82
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• 2015

Generally, the optimal solution of assignment problem can be obtained by Hungarian algorithm of two-sided optimization with time complexity $O(n^4)$. This paper suggests one-sided optimal assignment and swap optimization algorithm with time complexity $O(n^2)$ can be achieve the goal of two-sided optimization. This algorithm selects the minimum cost for each row, and reassigns over-assigned to under-assigned cell. Next, that verifies the existence of swap optimization candidates, and swap optimizes with ${\kappa}-opt({\kappa}=2,3)$. For 27 experimental data, the swap-optimization performs only 22% of data, and 78% of data can be get the two-sided optimal result through one-sided optimal result. Also, that can be improves on the solution of best known solution for partial problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.255-262
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• 2015

In the absence of a deterministic algorithm for economic load dispatch optimization problem (ELDOP), existing algorithms proposed as solutions are inevitably non-deterministic heuristic algorithms. This paper, therefore, proposes a balance-and-swap algorithm to solve an ELDOP. Firstly, it balances the initial value to ${\Sigma}P_i=P_d$ by subsequently reducing power generation for each adult-step and baby-step and selects the minimum cost-generating method. Subsequently, it selects afresh the minimum cost-generating method after an optimization of the previously selected value with adult-step baby-step swap and giant-step swap methods. Finally, we perform the $P_i{\pm}{\beta}$, (${\beta}=0.1,0.01,0.001,0.0001$) swap. When applied to the 3 most prevalently used economic load dispatch problem data, the proposed algorithm has obtained improved results for two and a result identical to the existing one for the rest. This algorithm thus could be applied to ELDOP for it has proven to consistently yield identical results and to be applicable to all types of data.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.51-58
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• 2015

This paper suggests assignment-swap algorithm with time complexity O(mn) to obtain the optimal solution for large-scale of transportation problem (TP) with incomplete cost lists. Generally, the TP with complete cost lists can be solved with TSM (Transportation Simplex Method). But, we can't be solved for large-scale of TP with TSM. Especially. It is hard to solve for large-scale TP with incomplete cost lists using TSM. Therefore, experts simply using commercial linear programming package. Firstly, the proposed algorithm applies assignment strategy of transportation quantity to ascending order of transportation cost. Then, we reassign from surplus of supply to shortage of demand. Secondly, we perform the 2-opt and 1-opt swap optimization to obtain the optimal solution. Upon application to $31{\times}15$ incomplete cost matrix problem, the proposed assignment-swap algorithm more improves the solution than LINGO of commercial linear programming.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.97-103
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• 2016

This paper deals with the cycle time minimization problem that determines the productivity in printed circuit board (PCB) with n components using the m placement machines. This is known as production cycle time determination problem (PCTDP). The polynomial time algorithm to be obtain the optimal solution has been unknown yet, therefore this hard problem classified by NP-complete. This paper gets the initial assignment result with the machine has minimum unit placement time per each component firstly. Then, the balancing process with reallocation from overhead machine to underhead machine. Finally, we perform the swap optimization and get the optimal solution of cycle time $T^*$ within O(mn) computational complexity. For experimental data, the proposed algorithm can be obtain the same result as integer programming+branch-and-bound (IP+B&B) and B&B.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.219-224
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• 2017

In facing the lack of a deterministic algorithm for economic load dispatch optimization problem, only non-deterministic heuristic algorithms have been suggested. Worse still, there is a near deficiency of research devoted to real-time load dispatch optimization algorithm. In this paper, therefore, I devise a shut-off and swap algorithm to solve real-time load dispatch optimization problem. With this algorithm in place, generators with maximum cost-per-unit generation power are to be shut off. The proposed shut-off criteria use only quadratic function in power generation cost function without valve effect nonlinear absolute function. When applied to the most prevalent economic load dispatch benchmark data, the proposed algorithm is proven to largely reduce the power cost of known algorithms.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.80-89
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• 2013

This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using a 'Mean-Variance Optimization (MVO)' algorithm with Kuhn-Tucker condition and swap process. The aim of the ED problem, one of the most important activities in power system operation and planning, is to determine the optimal combination of power outputs of all generating units so as to meet the required load demand at minimum operating cost while satisfying system equality and inequality constraints. This paper applies Kuhn-Tucker condition and swap process to a MVO algorithm to improve a global minimum searching capability. The proposed MVO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones, transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. The results are compared with those of the state-of-the-art methods as well.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.243-250
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• 2014

In this paper, I devise a balance-swap optimization (BSO) algorithm to solve economic load dispatch with a quadratic fuel cost function. This algorithm firstly sets initial values to $P_i{\leftarrow}P_i^{max}$, (${\Sigma}P_i^{max}$ > $P_d$) and subsequently entails two major processes: a balance process whereby a generator's power i of $_{max}\{F(P_i)-F(P_i-{\alpha})\}$, ${\alpha}=_{min}(P_i-P_i^{min})$ is balanced by $P_i{\leftarrow}P_i-{\alpha}$ until ${\Sigma}P_i=P_d$; and a swap process whereby $_{max}\{F(P_i)-F(P_i-{\beta})\}$ > $_{min}\{F(P_i+{{\beta})-F(P_j)\}$, $i{\neq}j$, ${\beta}$ = 1.0, 0.1, 0.1, 0.01, 0.001 is set at $P_i{\leftarrow}P_i-{\beta}$, $P_j{\leftarrow}P_j+{\beta}$. When applied to 15, 20, and 38-generators benchmark data, this simple algorithm has proven to consistently yield the best possible results. Moreover, this algorithm has dramatically reduced the costs for a centralized operation of 73-generators - a sum of the three benchmark cases - which could otherwise have been impossible for independent operations.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.189-196
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• 2012

This paper proposes Swap algorithm for solving Dynamic Economic Dispatch (DED) problem. The proposed algorithm initially balances total load demand $P_d$ with total generation ${\Sigma}P_i$ by deactivating a generator with the highest unit generation cost $C_i^{max}/P_i^{max}$. It then swaps generation level $P_i=P_i{\pm}{\Delta}$, (${\Delta}$=1.0, 0.1, 0.01, 0.001) for $P_i=P_i-{\Delta}$, $P_j=P_j+{\Delta}$ provided that $_{max}[F(P_i)-F(P_i-{\Delta})]$ > $_{min}[F(P_j+{\Delta})-F(P_j)]$, $i{\neq}j$. This new algorithm is applied and tested to the experimental data of Dynamic Economic Dispatch problem, demonstrating a considerable reduction in the prevalent heuristic algorithm's optimal generation cost and in the maximization of economic profit.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.155-162
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• 2015

This paper proposes a deterministic optimization algorithm to solve economic load dispatch problem with quadratic convex fuel cost function. The proposed algorithm primarily partitions a generator with prohibited zones into multiple generators so as to place them afield the prohibited zone. It then sets initial values to $P_i{\leftarrow}P_i^{max}$ and reduces power generation costs of those incurring the maximum unit power cost. It finally employs a swap optimization process of $P_i{\leftarrow}P_i-{\beta}$, $P_j{\leftarrow}P_j+{\beta}$ where $_{max}\{F(P_i)-F(P_i-{\beta})\}$ > $_{min}\{F(P_j+{\beta})-F(P_j)\}$, $i{\neq}j$, ${\beta}=1.0,0.1,0.01,0.001$. When applied to 3 different 15-generator cases, the proposed algorithm has consistently yielded optimized results compared to those of heuristic algorithms.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.27-35
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• 2021

Local search has been used to solve various combinatorial optimization problems. One of the most important factors in local search is the method of generating a neighbor solution. In this paper, we propose neighbor generation strategies of local search for permutation-based combinatorial optimization, and compare the performance of each strategies targeting the traveling salesman problem. In this paper, we propose a total of 10 neighbor generation strategies. Basically, we propose 4 new strategies such as Rotation in addition to the 4 strategies such as Swap which have been widely used in the past. In addition, there are Combined1 and Combined2, which are made by combining basic neighbor generation strategies. The experiment was performed by applying the basic local search, but changing only the neighbor generation strategy. As a result of the experiment, it was confirmed that the performance difference is large according to the neighbor generation strategy, and also confirmed that the performance of Combined2 is the best. In addition, it was confirmed that Combined2 shows better performance than the existing local search methods.