• Proceedings of the Korea Database Society Conference
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• 2000.11a
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• pp.27-37
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• 2000

o An Enriched conceptualization of BM o Needs further research on: - An Integrative framework for BM research - A dynamic. evolutionary account of BM - Empirical validation of the analysis models - Draw practical implications and guidelines for business (omitted)

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.11
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• pp.790-802
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• 2014

In cognitive radio technology, secondary users can determine the absence of PU by periodic sensing operation and cooperative sensing between SUs yields a significant sensing performance improvement. However, there exists a trade off between the gains in terms of probability of detection of the primary users and the costs of false alarm probability. Therefore, the cooperation group must maintain the suitable size. And secondary users should sense not only the currently using channels and but also other candidates channel to switch in accordance with sudden appearance of the primary user. In this paper, we propose an effective group cooperative sensing algorithm in distributed network situations that is considering both of inband and outband sensing using evolutionary game theory. We derived that the strategy group of secondary users converges to an ESS(Evolutionary sable state). Using a learning algorithm, each secondary user can converge to the ESS without the exchange of information to each other.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.1-9
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• 1997

These papers propose an evolutionary algorithm for re-using output of waste fuel of light water reactor system in nuclear power plants. Evolutionary algorithm is useful for optimization of the large space problem. The wastes contain several re-useable elements, and they should be carefully selected and blended to satisfy requirements as input material to the heavy water nuclear reactor system. This problem belongs to a NP-hard like the 0/1 Knapsack problem. Two evolutionary strategies are used as a, pp.oximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method, which performs the feasible teat and solution evaluation by using the vectorized data in problem. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.442-460
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• 2021

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE⁺ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

• Advances in Computational Design
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• v.7 no.1
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• pp.1-17
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• 2022

In this study, a new strategy is presented to transmit the fundamental elastic beam problem into the modern optimization platform and solve it by using artificial intelligence (AI) tools. As a practical example, deflection of Euler-Bernoulli beam is mathematically formulated by 2nd-order ordinary differential equations (ODEs) in accordance to the classical beam theory. This fundamental engineer problem is then transmitted from classic formulation to its artificial-intelligence presentation where the behavior of the beam is simulated by using neural networks (NNs). The supervised training strategy is employed in the developed NNs implemented in the heuristic optimization algorithms as the fitness function. Different evolutionary optimization tools such as genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve this non-linear optimization problem. The step-by-step procedure of the proposed method is presented in the form of a practical flowchart. The results indicate that the proposed method of using AI toolsin solving beam ODEs can efficiently lead to accurate solutions with low computational costs, and should prove useful to solve more complex practical applications.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1106-1110
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• 2010

This paper proposes a method of optimizing a stacking strategy for an automated container terminal using multi-objective evolutionary algorithms (MOEAs). Since the yard productivities of seaside and landside are conflicting objectives to be optimized, it is impossible to maximize them simultaneously. Therefore, we derive a Pareto optimal set instead of a single best solution using an MOEA. Preliminary experiments showed that the population is frequently stuck in local optima because of the difficulty of the given problem depending on the yard occupancy rate. To cope with this problem, we propose another method of simultaneously optimizing two problems with different difficulties so that diverse solutions can be preserved in the population. Experimental results showed the proposed method can derive better stacking policies than the compared method solving a single problem given the same computational costs.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• Korean Institute of Interior Design Journal
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• v.20 no.1
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• pp.33-42
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• 2011

This study, by applying the experiential and psychological thought category of "impression" as the space design strategy for drawing out "sensitivity," aims to break down the psychological mechanism forming the image held by the subject, and based on such segmentation, seek and strategize an effective design methodology. The subject's memory, pure sensation, and movement sensation are set as the preceding determinants comprising the psychological mechanism of space impression. The control determinants for achieving activation of psychological effect are reinterpreted as the relations of memory-cognition, sensation-perception, and movement-sensation and categorized into a cognitive strategy based on association, metaphor and remembrance; a direct sensation strategy based on formation, superposition and transposition of sensation; and a sensation strategy of process-formation based on expectation organization and reversal, contraction and relaxation. Each strategy is achieved through the formal, material and structural control of architecture. As a restructuring of the design methodology that has been experientially applied, the strategy categorization is important in that, by presenting the usefulness and effect by strategy according to space experience, it makes concrete the basic data that are useful for designing experience of sensitivity.

• Journal of Korean Society for Quality Management
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• v.39 no.1
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• pp.98-108
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• 2011

Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining, pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.4
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• pp.55-79
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• 2010

This study deals with a model for platform competition in a two-sided market. We suppose there are both direct and indirect network externalities between suppliers and users of each platform. Moreover, we suppose that both users and suppliers are distributed in their relative affinity for each platform type. That is, each user [supplier] has his/her own preferential position toward each platform, and users [suppliers] are horizontally differentiated over [0, 1]. And for analytical tractability, some parameters like direct and indirect network externalities are the same across the markets. Given the parameters and the pricing profile, users and suppliers conduct subscription game, where participants select the platform that gives them the highest payoffs. This game proceeds according to a replicator dynamics of the evolutionary game, which is simplified by properly defining gains from participant's strategy in the subscription game. We find that depending on the strength of these network effects, there might either be multiple stable equilibria, at which users and suppliers distribute across both platforms, or one unstable interior equilibrium corresponding to the market tipping in favor of either platform. In both cases, we also consider the pricing power of competing platform providers under the framework of the Stackelberg game. In particular, our study examines the possible effects of the type of competition between platform providers, which may constrain the equilibrium selection in the subscription game.