• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.315-317
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• 2001

This paper studies for the optimal operation of BESS. The goal must be optimized electricity charge of the customer sides owned time-of-use rates in this paper. Therefore, the least of cost is caused by BESS installation, Multi-Pass Dynamic Programming (MPDP) algorithm is applied to the customer for the optimal operation determination in this paper. It is to solve the optimal solution under the constraints. No matter how become one stage in general, problem is divided into several stage in series in this algorithm. Regardless of the decision step, MPDP is only accomplished based on the state of stage in the present. To investigate the efficiencies of the algorithm, it is applied the typical load curve to the cutomer owned Time-Of-Use(TOU). Result shows that the maximun economic benefits of the battery energy storage system can be achieved by the purposed algorithm.

• Journal of Ocean Engineering and Technology
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• v.29 no.3
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• pp.263-269
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• 2015

This paper proposes a new approach for solving the weather routing problem by dividing it into two phases with the goal of fuel saving. The problem is to decide two optimal variables: the heading angle and speed of the ship under several constraints. In the first phase, the optimal route is obtained using the Real-Time Adaptive A* algorithm with a fixed ship speed. In other words, only the heading angle is decided. The second phase is the speed scheduling phase. In this phase, the original problem, which is a nonlinear optimization problem, is converted into a geometric programming problem. By solving this geometric programming problem, which is a convex optimization problem, we can obtain an optimal speed scheduling solution very efficiently. A simple case of numerical simulation is conducted in order to validate the proposed method, and the results show that the proposed method can save fuel compared to a constant engine output voyage and constant speed voyage.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.11
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• pp.1104-1111
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• 2009

In the low altitude, mission of the aircraft is restricted by a variety of threats such as anti-air missiles and terrain obstacles. Especially, aircraft have always a risk of ground collision near terrain. In this study, to effectively solve this problem, we developed the flight path generation algorithm that is considered the terrain avoidance. In this flight path generation algorithm, waypoints that should be passed by the UAV are selected first. The waypoints are located in the middle of the terrain obstacles. Then, physically meaningful waypoints sets are classified by Dijkstra algorithm. The optimal waypoint guidance law based on the optimal control theory is applied to produce trajectory candidates. And finally the minimum control energy trajectory is determined.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1095-1101
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• 2020

Vessels or aircraft should be loaded with containers or cargo to maintain weight balance in order to be stable when navigating the route. The container loading algorithm is known as the NP problem and several heuristic methods have been studied. Containers can be characterized by the uniform volume and weight, which makes it easier to find an optimal loading method. In this paper, we propose an algorithm for weight balance when the volume and weight of an object are uniform. It is assumed that the loading space has a special structure of m * n mesh (where m and n are both odd). In this case, we designed a greedy algorithm and proved that the algorithm is optimal in that it can always find a loading position that maintains a weight balance regardless of the number of objects. Our algorithm can be used in many engineering problems, such as loading algorithms and load balancing problems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.265-275
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• 2023

The purpose of this paper is to devise an optimal route allocation algorithm for automated vehicle(AV) in monitoring quality of road infrastructure to support the road safety. The tasks of an AV in this paper include visiting node-links at least once during its operation and checking status of road infrastructure, and coming back to its depot.. In selecting optimal route, its priority goal is visiting the node-links with higher risks while reducing costs caused by operation. To deal with the problem, authors devised reward maximizing algorithm for AVs. To check its validity, the authors developed simple toy network that mimic node-link networks and assigned costs and rewards for each node-link. With the toy network, the reward maximizing algorithm worked well as it visited the node-link with higher risks earlier then chinese postman route algorithm (Eiselt, Gendreau, Laporte, 1995). For further research, the reward maximizing algorithm should be tested its validity in a more complex network that mimic the real-life.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.2
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• pp.111-119
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• 1995

In this paper, we propose an efficient SA(simulated annealing) algorithm for the synthesis of binary phase computer generated hologram. SA algorithm is a method to find the optimal solution through iterative technique. It is important that selecting cost function and parameters within this algorithm. The aplications of converentional SA algorithm to synthesize parameters within this algorithm. The applications of conventional SA algorithm to synthesize binary hologram have many problems because of inappropriate paramters and cost function. So, we propose a new cost function and a calculation technique of proper parameters required to achieve the optimal solution. Computer simulation results show that the proposed method is better than conventional method in terms of diffraction efficiency and reconstruction error. Also, we show the reconstructed images by the proposed method through optical esperiment.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.73-82
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• 1988

This research is concerned with production scheduling for FMS (Flexible Manufacturing System) which consists of machine centers served by cycle conveyor. The objective of the research is to develop and evaluate scheduling procedures to minimize the mean flow time. An optimal algorithm called SCTF (Shortest Circle Time First) is proposed when the conveyor runs at minimum possible speed (CS=1) and a heuristic algorithm called SCTJMF (Shortest Cycle Time and Job Matching Algorithm) is suggested when the conveyor runs at double speed (CS=2). The evaluation of the heuristic algorithm was implemented by comparison with the optimal algorithm for 112 experimentations for CS=1 and random schedule. The results showed that the proposed heuristic algorithm provides better solution that can be regarded noticeable when compared with SCTF algorithm and random scheduling.