• Journal of KIISE
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• v.41 no.12
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• pp.1090-1098
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• 2014

In this paper, an improved ACDE (Adaptive Cauchy Differential Evolution) algorithm with faster convergence speed, called ACDE2, is suggested. The baseline ACDE algorithm uses a "DE/rand/1" mutation strategy to provide good population diversity, and it is appropriate for solving multimodal optimization problems. However, the convergence speed of the mutation strategy is slow, and it is therefore not suitable for solving unimodal optimization problems. The ACDE2 algorithm uses a "DE/current-to-best/1" mutation strategy in order to provide a fast convergence speed, where a control parameter initialization operator is used to avoid converging to local optimization. The operator is executed after every predefined number of generations or when every individual fails to evolve, which assigns a value with a high level of exploration property to the control parameter of each individual, providing additional population diversity. Our experimental results show that the ACDE2 algorithm performs better than some state-of-the-art DE algorithms, particularly in unimodal optimization problems.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.435-447
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• 2006

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel AEA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1757-1772
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• 2020

As the smartphone market becomes saturated, an innovative device equipped with new features is expected to appear soon in mobile communications. In particular, various possibilities were raised regarding the alternative technologies that can develop post-smartphones, which are differentiated from the current smartphones, as Korea commercialized the 5G infrastructure for the first time in the world. Under these circumstances, the Korean government announced the "5G+ Strategy for Realizing Innovative Growth" in April 2019, vowing to build an innovative industrial ecosystem quickly while creating various convergence services based on the 5G infrastructure. As described above, the policy importance of the alternative technologies that will develop post-smartphones is increasing, but the theoretical study on the technology evolution of post-smartphones has not been systematically conducted until now. This study reviewed the alternative technologies that can develop post-smartphones through documentary research, and data mining analysis was performed on the research result using actual data. The policy priority was also set quantitatively for the alternative technologies of post-smartphones in order to determine the alternative post-smartphone technology that the government should focus on given the constraint of limited resources. As a results, autonomous vehicle(43.68%) was found to be most important, followed by artificial intelligence(17.4%) and Internet of Things(13.1%), among alternative technologies that could develop into the post-smartphone.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.299-311
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• 2015

Nowadays, the distribution centres aim to reduce costs by reducing inventory and timely shipment. Cross docking is a logistics strategy in which products delivered to a distribution centre by inbound trucks are directly unloaded and transferred to outbound trucks with minimum warehouse storage. Moreover, on-time delivery in a distribution network becomes very crucial especially when several distribution centres and customers are involved. Therefore, an efficient truck scheduling is needed to synchronize the delivery throughout the network in order to satisfy all stake-holders. This paper presents a mathematical model of a mixed integer programming for door assignment and truck scheduling in a multiple inbound and outbound doors cross docking problem according to Just-In-Time concept. The objective is to find the schedule of transhipment operations to simultaneously minimize the total earliness and total tardiness of trucks. Then, a multi-objective differential evolution (MODE) is proposed with an encoding scheme and four decoding strategies, called ITSH, ITDD, OTSH and OTDD, to find a Pareto frontier for the multi-door cross docking problems. The performances of MODE are evaluated using 15 generated instances. The numerical experiments demonstrate that the proposed algorithm is capable of finding a set of diverse and high quality non-dominated solutions.

• Smart Structures and Systems
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• v.14 no.1
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• pp.17-37
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• 2014

In this study, four widely accepted and used variants of Evolution Strategies (ES) are adapted and applied to the output-error state-space identification problem. The selection of ES is justified by prior strong indication of superior performance to similar problems, over alternatives like Genetic Algorithms (GA) or Evolutionary Programming (EP). The ES variants that are being tested are (i) the (1+1)-ES, (ii) the $({\mu}/{\rho}+{\lambda})-{\sigma}$-SA-ES, (iii) the $({\mu}_I,{\lambda})-{\sigma}$-SA-ES, and (iv) the (${\mu}_w,{\lambda}$)-CMA-ES. The study is based on a six-degree-of-freedom (DOF) structural model of a shear building that is characterized by light damping (up to 5%). The envisaged analysis is taking place through Monte Carlo experiments under two different excitation types (stationary / non-stationary) and the applied ES are assessed in terms of (i) accurate modal parameters extraction, (ii) statistical consistency, (iii) performance under noise-corrupted data, and (iv) performance under non-stationary data. The results of this suggest that ES are indeed competitive alternatives in the non-linear state-space estimation problem and deserve further attention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.13-30
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• 2008

The objective of this study is to explore a knowledge sharing typology for Internet portals based on knowledge-based view of firm. Furthermore, we provide insights into how the evolution of Internet portals takes place by describing user behavior of knowledge sharing. For doing this, we first present a typology of knowledge sharing based on the two dimensions such as knowledge donation and knowledge collection. Then we conduct case study of the Korean major portals to demonstrate a proposed typology. The main finding of the analysis is that three distinctive types of knowledge sharing patterns within portals are distinguished: collaboration, accumulation, and publishing. We conclude that user behavior of knowledge sharing is characterized as guiding factors in evolution process.

• Coupled systems mechanics
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• v.3 no.1
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• pp.1-25
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• 2014

In this work, we present the theoretical formulation, operator split solution procedure and partitioned software development for the coupled thermomechanical systems. We consider the general case with nonlinear evolution for each sub-system (either mechanical or thermal) with dedicated time integration scheme for each sub-system. We provide the condition that guarantees the stability of such an operator split solution procedure for fully nonlinear evolution of coupled thermomechanical system. We show that the proposed solution procedure can accommodate different evolution time-scale for different sub-systems, and allow for different time steps for the corresponding integration scheme. We also show that such an approach is perfectly suitable for parallel computations. Several numerical simulations are presented in order to illustrate very satisfying performance of the proposed solution procedure and confirm the theoretical speed-up of parallel computations, which follow from the adequate choice of the time step for each sub-problem. This work confirms that one can make the most appropriate selection of the time step with respect to the characteristic time-scale, carry out the separate computations for each sub-system, and then enforce the coupling to preserve the stability of the operator split computations. The software development strategy of direct linking the (existing) codes for each sub-system via Component Template Library (CTL) is shown to be perfectly suitable for the proposed approach.

• Journal of the Korean institute of surface engineering
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• v.54 no.4
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• pp.164-170
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• 2021

Vanadium redox flow batteries (VRFB) have emerged as large-scale energy storage systems (ESS) due to their advantages such as low cross-contamination, long life, and flexible design. However, Hydrogen evolution reaction (HER) in the negative half-cell causes a harmful influence on the performance of the VRFB by consuming current. Moreover, HER hinders V²⁺/V³⁺ redox reaction between electrode and electrolyte by forming a bubble. To address the HER problem, carbon nanotube/graphite felt electrode (CNT/GF) with oxygen functional groups was synthesized through the hydrothermal method in the H₂SO₄ + HNO₃ (3:1) mixed acid solution. These oxygen functional groups on the CNT/GF succeed in suppressing the HER and improving charge transfer for V²⁺/V³⁺ redox reaction. As a result, the oxygen functional group applied electrode exhibited a low overpotential of 0.395 V for V²⁺/V³⁺ redox reaction. Hence, this work could offer a new strategy to design and synthesize effective electrodes for HER suppression and improving the energy density of VRFB.