• Korean Journal of Computational Design and Engineering
• /
• v.19 no.4
• /
• pp.410-422
• /
• 2014

Unlike other weapon systems, a naval vessel has unique characteristics in that the vessel itself is a naval unit. In limited space, compartments with various objectives and characteristics need to be arranged, so that vessel performance is maximized. This paper studied a compartment arrangement algorithm that considers activity relationships among compartments and survivability of a vessel. Based on the study, a compartment arrangement application is developed that can generate various layout alternatives swiftly. The application developed in this study aims at automating a two dimensional compartment layout problem. A combinatorial optimization is performed with the differential evolution algorithm to achieve the optimized layout.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.12
• /
• pp.5704-5708
• /
• 2012

In this paper, the algorithm to determine the optimal condition of traverse grinding is proposed by using differential evolution algorithm(DEA). The cost function to determine the optimal grinding condition is designed with considering process cost, production rate, surface roughness. Also, the constraint conditions for grinding such as thermal damage effect, machine tool stiffness, wear parameter of grinding wheel, surface roughness are considered. The algorithm is implemented with LabView software which is widely used at the industrial field. The performance of proposed algorithm is verified by comparing with the result of genetic algorithm(GA) through computer simulation.

• Advances in Computational Design
• /
• v.1 no.4
• /
• pp.315-327
• /
• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.4
• /
• pp.139-155
• /
• 2006

The facility location problem of this paper is distinguished from the maximal covering location problem and the flxed-charge facility location problem. We propose the maximal profiting location problem (MPLP) that is the facility location problem maximizing profit with multi-product. We apply to the simulated annealing algorithm, the stochastic evolution algorithm and the accelerated simulated annealing algorithm to solve this problem. Through a scale-down and extension experiment, the MPLP was validated and all the three algorithm enable the near optimal solution to produce. As the computational complexity is increased, it is shown that the simulated annealing algorithm' is able to find the best solution than the other two algorithms in a relatively short computational time.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.4
• /
• pp.265-276
• /
• 2004

Genetic Algorithm(GA), that is shown stable performance to find an optimal solution, has been used as a method of solving large-scaled optimization problems with complex constraints in various applications. Since it takes so much time to execute a long computation process for iterative evolution and adaptation. In this paper, a hardware-based adaptive GA was proposed to reduce the serious computation time of the evolutionary process and to improve the accuracy of convergence to optimal solution. The proposed GA, based on steady-state model among continuos generation model, performs an adaptive mutation process with consideration of the evolution flow and the population diversity. The drawback of the GA, premature convergence, was solved by the proposed adaptation. The Performance improvement of convergence accuracy for some kinds of problem and condition reached to 5-100% with equivalent convergence speed to high-speed algorithm. The proposed adaptive GAP(Genetic Algorithm Processor) was implemented on FPGA device Xilinx XCV2000E of EHW board for face recognition.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.3
• /
• pp.185-190
• /
• 2002

In distributed autonomous robotic systems (DARS), each robot must behave by itself according to its states ad environments, and if necessary, must cooperate with other robots in order to carry out their given tasks. Its most significant merit is that they determine their behavior independently, and cooperate with other robots in order to perform the given tasks. Especially, in DARS, it is essential for each robot to have evolution ability in order to increase the performance of system. In this paper, a schema co-evolutionary algorithm is proposed for the evolution of collective autonomous mobile robots. Each robot exchanges the information, chromosome used in this algorithm, through communication with other robots. Each robot diffuses its chromosome to two or more robots, receives other robot's chromosome and creates new species. Therefore if one robot receives another robot's chromosome, the robot creates new chromosome. We verify the effectiveness of the proposed algorithm by applying it to cooperative search problem.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.1
• /
• pp.7-12
• /
• 2013

This paper proposed the optimal algorithm of grinding system and the method to realize it. The optimal function was proposed in order to design the optimal grinding process. DE(Differential Evolution) algorithm was used to obtain the selective optimal function. The realization of algorithm was implemented by LabView software used widely at industrial field and the proposed algorithm was verified for through computer simulation. The result of the proposed algorithm can be used for the guide line of the grinding process.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.4
• /
• pp.302-309
• /
• 2005

We have studied a model and application that evolves the behavioral knowledge of a virtual robot. The knowledge is represented in classification rules and a neural network, and is learned by a genetic algorithm. The model consists of a virtual robot with behavior knowledge, an environment that it moves in, and an evolution performer that includes a genetic algorithm. We have also applied our model to an environment where the robots gather food into a nest. When comparing our model with the conventional method on various test cases, our model showed superior overall learning.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.591-597
• /
• 2007

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new polygon based Q-learning algorithm and distributed genetic algorithms are proposed for behavior learning and evolution of collective autonomous mobile robots. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.82-85
• /
• 2003

In this paper, in order to deduce the deep structure of a set of fuzzy rules from the surface structure, we use co-evolutionary algorithm based on modified Nash GA. This algorithm coevolves membership functions in antecedents and parameters in consequents of fuzzy rules. We demonstrate this co-evolutionary algorithm and apply to the mobile robot control. From the result of simulation, we compare modified Nash GA with the other co-evolution algorithms and verify the efficacy of this algorithm through application to fuzzy systems.