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This paper presents a novel approach to optimizing rural electrical power transmission network. The primary objective of this project is to determine an optimal (more economical) network configuration for a case study where large portions of network assets are approaching the end of their life cycle. Therefore, this is the most opportune time to redesign, and implement economically beneficial distribution alternatives such as microgrids and standalone power systems and, to evaluate the economic benefits of a combination of distribution alternatives. The latter alongside multiple microgrids have often been overlooked by past studies. Using Minimum Spanning Trees (MSTs) and clustering algorithms, the ideal location of microgridsand combination of distribution alternatives can be investigated. The results obtained from this study suggest that implementing microgrids and standalone power systems, drastically reduce the total cost of the network when compared to anoverhead transmission MST network. Furthermore, a combination of an overhead network with microgrids and standalone power systems resulted in the most economical network configurations.Index Terms-Distribution network, K-means, microgrids, standalone power systems.

The main purpose of this study is to propose a portfolio investment strategy based on investor types information. For improvement of investment performance, artificial intelligence techniques are used to construct a portfolio. Among many artificial intelligence techniques, cluster analysis is applied to select securities and genetic algorithm is applied to assign the respective weight within the portfolio. Empirical experiments in the Korean stock market show that proposed portfolio investment strategy is practicable and superior strategy. This result implies that analysis of investor's trading behavior may assist investors to make an investment decision and to get superior performance.