• The Korean Journal of Petroleum Geology
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• v.9 no.1_2 s.10
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• pp.24-39
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• 2001

Prospecting for energy and mineral resources is essential kind of public fundamentals that manage the nation's economy. Most explorations in the past were concentrated in the simple structural traps in relatively shallow depth. Due to their vast exploitation, recent history has shown that the emphasis in explorations has steadily shifted toward the subtle stratigraphic traps in deeper level. Increasing exploration for the subtle stratigraphic traps in deeper level requires precise correlation and assessment of deeply buried strata in the basin. However, the descriptive stratigraphic principles used for evaluation of the simple structural traps are limited to delineate the subtle stratigraphic traps in deeper depth. As this occurs, it is imperative to establish a new stratigrtaphic paradigm that allows a more sophisticated understanding on the basin stratigraphy. This study provides an exemplary application of integrated stratigraphic approach to defining basin stratigraphy of the Middle Ordovician Taebacksan Basin and the Cretaceous South Yellow Sea Basin, Korea. The integrated stratigraphic approach gives much better insight to unravel the stratigraphic response to tectonic evolution of the basins, which can be utilized for enhancing the efficiency of resources exploration and development in the basins. Thus, the integrated stratigraphic approach should be considered as a new stratigraphic norm that can improve the probability of success in any type of resources exploration and development project.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.1
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• pp.13-27
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• 2016

Companies strive to have the ability to flexibly respond to environmental changes in modern society with its rapidly changing business environment. That is, companies try to achieve the corporate performance by using a variety of strategies since companies that don't go along with changes in industry are likely to fall behind. Also, the corporate performance is a key element in national competitiveness, and government is willing to support companies to maximize their performance in various ways. This study examines whether there is a difference between corporate strategies and government policies according to the retention and the type of certification of innovation. The company's strategy configuration effort is largely divided into exploration and exploitation of external knowledge, while the government's policy is divided into direct support, indirect support, and financial support. The corporate performance is analyzed using technological performance; innovative perspective and the sales; and the actual corporate performance as proxy variables. As a result, the variable affecting the performance differs according to the retention of certification of innovation. The variable affecting the corporate performance differs according to the type of certification of innovation as well. Therefore, it was found that companies can achieve the corporate performance by considering the situation at hand and the differentiated action strategies depending on the type of certification of innovation.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Economic and Environmental Geology
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• v.36 no.2
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• pp.59-74
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• 2003

Prospecting for energy and mineral resources is essential kind of public fundamentals that manage the nation's economy. Most explorations in the past were concentrated in the simple structural traps in relatively shallow depth. Due to their vast exploitation, recent history has shown that the emphasis in explorations has steadily shifted toward the subtle stratigraphic traps in deeper level. Increasing exploration for the subtle stratigraphic traps in deeper level requires precise correlation and assessment of deeply buried strata in the basin. However, the descriptive strati-graphic principles used for evaluation of the simple structural traps are limited to delineate the subtle stratigraphic traps in deeper depth. As this occurs. it is imperative to establish a new stratigraphic paradigm that allows a more sophisticated understanding on the basin stratigraphy. This study provides an exemplary application of integrated stratigraphic approach to defining basin history of the Middle Ordovician Taebacksan Basin, Korea. The integrated stratigraphic approach gives much better insight to unravel the stratigraphic response to tectonic evolution of the basins, which can be utilized fer enhancing the efficiency of resources exploration and development in the basins. Thus, the integrated stratigraphic approach should be emphasized as a new stratigraphic norm that can improve the probability of success in any type of resources exploration and development project.

• Economic and Environmental Geology
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• v.41 no.5
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• pp.475-484
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• 2008

In this study, we analyzed national mining investment security and country risk, and suggested a new index for exploration and development investment for mining projects in abroad by an analysis of relationship between these grades and mineral resource development investment. For this, potential risks for mining in mineral-rich countries are assessed, and the risk of the exploration and development investment for mining projects is relatively evaluated by OECD country risk. It is noted that countries of the lower ranks in OECD are consistently good agreement with the high grade in Behre Dolbear Group Inc. for favorable mineral exploitation, whereas the higher ranks have shown diverse and high risks for the mining investigation and development. Consequently, it is necessary that assessment of the relationship between mineral resource index and country risk for mining projects to be investigated and developed in future should be applied before business decision of mineral investigation projects in abroad.

• Structural Engineering and Mechanics
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• v.50 no.3
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• pp.257-273
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• 2014

This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and $CO_2$ emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded $CO_2$ emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.137-149
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• 2020

Several multi-agent reinforcement learning (MARL) algorithms have achieved overwhelming results in recent years. They have demonstrated their potential in solving complex problems in the field of real-time strategy online games, robotics, and autonomous vehicles. However these algorithms face many challenges when dealing with massive problem spaces in sparse reward environments. Based on the centralized training and decentralized execution (CTDE) architecture, the MARL algorithms discussed in the literature aim to solve the current challenges by formulating novel concepts of inter-agent modeling, credit assignment, multiagent communication, and the exploration-exploitation dilemma. The fundamental objective of this paper is to deliver a comprehensive survey of existing MARL algorithms based on the problem statements rather than on the technologies. We also discuss several experimental frameworks to provide insight into the use of these algorithms and to motivate some promising directions for future research.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.79-88
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• 2015

Many servers, such as web and database servers, receive a continual stream of requests. The servers should schedule these requests to provide the best services to users. In this paper, a hybrid genetic algorithm is proposed for scheduling divisible requests without interruption in which the objective is to minimize the total stretch. The stretch of a request is the ratio of the amount of time the request spent in the system to its response time. The hybrid genetic algorithm adopts the idea of seed selection and development in order to improve the exploitation and exploration power of genetic algorithms. Extensive computational experiments have been conducted to compare the performance of the hybrid genetic algorithm with that of genetic algorithms.

• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.226-249
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• 2016

The symbiotic organisms search (SOS) algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.1940-1949
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• 2015

This paper presents an Enhanced Particle Swarm Optimization (EPSO) to solve short-term hydrothermal scheduling (STHS) problem with non-convex fuel cost function and a variety of operational constraints related to hydro and thermal units. The operators of the conventional PSO are dynamically controlled using exponential functions for better exploration and exploitation of the search space. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement in the conventional PSO. The effectiveness of the proposed method has been tested for STHS of two standard test generating systems while considering several operational constraints like system power balance constraints, power generation limit constraints, reservoir storage volume limit constraints, water discharge rate limit constraints, water dynamic balance constraints, initial and end reservoir storage volume limit constraints, valve-point loading effect, etc. The application results show that the proposed EPSO method is capable to solve the hard combinatorial constraint optimization problems very efficiently.