• 한국컴퓨터정보학회논문지
• /
• 제26권10호
• /
• pp.27-35
• /
• 2021

지역 탐색은 다양한 조합 최적화 문제들을 해결하기 위해 활용되어 왔다. 지역 탐색에 있어서 가장 중요한 요소 중 하나가 이웃해를 생성하는 방법이다. 본 논문에서는 순열 기반 조합 최적화를 위한 지역 탐색의 이웃해 생성 전략들을 제안하고, 순회 외판원 문제를 대상으로 각 전략들의 성능을 비교한다. 본 논문에서는 총 10가지 이웃해 생성 전략을 제안한다. 기본적으로 기존에 많이 사용했던 Swap 등 4가지 전략 이외에 Rotation 등 4가지 기법을 새롭게 제안한다. 이외에 기본 이웃해 생성 전략들을 결합하여 만든 Combined1과 Combined2가 있다. 실험은 기본적인 지역 탐색을 적용하되 이웃해 생성 전략만 변경하여 수행하였다. 실험 결과, 이웃해 생성 전략에 따라 성능 차이가 큰 것을 확인하였으며 아울러 Combined2의 성능이 가장 좋음을 확인하였다. 뿐만 아니라 Combined2는 기존의 지역 탐색 기법들보다 더 좋은 성능을 발휘함을 확인하였다.

• KIEE International Transactions on Power Engineering
• /
• 제5A권4호
• /
• pp.331-338
• /
• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• 한국산업정보학회논문지
• /
• 제27권2호
• /
• pp.61-69
• /
• 2022

다수 에이전트 시스템(Multi-agent system)은 에이전트 각자의 결정으로 최상의 조직화 된 결정을 달성하는 것을 목표로 하는 시스템으로 본 논문에서는 다수 에이전트-다수 작업의 할당 문제를 제시한다. 본 문제는 각 에이전트가 하나의 작업에 할당이 되어 수행하고, 작업 수행에 대한 작업 완료 확률(completion probability)이 있으며 모든 작업의 수행 확률을 최대화하는 할당을 결정한다. 비선형(non-linearity)의 목적함수와 조합 최적화(combinatorial optimization)로 표현되는 본 문제는 NP-hard로, 효과적이면서 효율적인 문제 해결 방법론 제시가 필요하다. 본 연구에서는 한계 이익(marginal gain)의 감소를 의미하는 하위모듈성(submodularity)을 활용한 근사 알고리즘(approximation algorithm)을 제안하고, 확장성(scalability)과 강건성(robustness) 측면에서 우수한 알고리즘임을 이론 및 실험적으로 제시한다.

• Journal of Electrical Engineering and Technology
• /
• 제6권2호
• /
• pp.174-181
• /
• 2011

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

• International Journal of Computer Science & Network Security
• /
• 제21권4호
• /
• pp.33-40
• /
• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• International Journal of Control, Automation, and Systems
• /
• 제3권2호
• /
• pp.236-243
• /
• 2005

This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

• 대한전기학회논문지:전력기술부문A
• /
• 제51권5호
• /
• pp.217-224
• /
• 2002

This paper presents an algorithm to obtain an approximate optimal solution for the service restoration and load balancing of large scale radial distribution system in a real-time operation environment. Since the problem is formulated as a combinatorial optimization problem, it is difficult to solve a large-scale combinatorial optimization problem accurately within the reasonable computation time. Therefore, in order to find an approximate optimal solution quickly, the authors proposed an algorithm which combines optimization technique called cyclic best-first search with heuristic based feeder loadings balance index for computational efficiency and robust performance. To demonstrate the validity of the proposed algorithm, numerical calculations are carried out the KEPCO's 108 bus distribution system.

• 경영과학
• /
• 제15권2호
• /
• pp.153-159
• /
• 1998

Clustering or classification is a very fundamental task that may occur almost everywhere for the purpose of grouping. Optimal clustering is an example of very complicated combinatorial optimization problem and it is hard to develop a generally applicable optimal algorithm. In this paper we propose a general-purpose algorithm for the optimal clustering based on SA(simulated annealing). Among various iterative global optimization techniques imitating natural phenomena that have been proposed and utilized successfully for various combinatorial optimization problem, simulated annealing has its superiority because of its convergence property and simplicity. We first present a version of accelerated simulated annealing(ASA) and then we apply ASA to develop an efficient clustering algorithm. Application examples are also given.

• 대한산업공학회지
• /
• 제42권6호
• /
• pp.386-394
• /
• 2016

In this research, in order to optimize the multi-objective function effectively, we suggested the optimization model to maximize the total destruction of ground targets and minimize the total damage of aircrafts and cost of air munitions by using goal programming. To satisfy the various variables and constraints of this mathematical model, the concept of air strike package is applied. As a consequence, effective attack can be possible by identifying the prior ground targets more quickly. This study can contribute to maximize the ROK air force's combat power and preservation of high value air asset in the war.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (A)
• /
• pp.892-894
• /
• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.