• Structural Engineering and Mechanics
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• v.10 no.4
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• pp.313-336
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• 2000

The focus of this paper is on the development and implementation of a methodology for automated design of discrete structural systems. The research is aimed at utilizing Genetic Algorithms (GA) as an automated design tool. Several key enhancements are made to the simple GA in order to increase the efficiency, reliability and accuracy of the methodology for code-based design of structures. The AISC-ASD design code is used to illustrate the design methodology. Small as well as large-scale problems are solved. Simultaneous sizing, shape and topology optimal designs of structural framed systems subjected to static and dynamic loads are considered. Comparisons with results from prior publications and solution to new problems show that the enhancements made to the GA do indeed make the design system more efficient and robust.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.101-117
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• 2007

In this paper, we propose a customized genetic algorithm (GA) to find the minimum-cost guideway network (GN) of personal rapid transit (PRT) subject to connectivity, reliability, and traffic capacity constraints. PRT is a novel transportation concept, where a number of automated taxi-sized vehicles run on an elevated GN. One of the most important problems regarding PRT is how to design its GN topology for given station locations and the associated inter-station traffic demands. We model the GN as a directed graph, where its cost, connectivity, reliability, and node traffics are formulated. Based on this formulation, we develop the GA with special genetic operators well suited for the GN design problem. Such operators include steady state selection, repair algorithm, and directed mutation. We perform numerical experiments to determine the adequate GA parameters and compare its performance to other optimization algorithms previously reported. The experimental results verify the effectiveness and efficiency of the proposed approach for the GN design problem having up to 210 links.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.451-460
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• 2012

Natural disasters often lead to regional failures that can cause network nodes and links co-located in a large geographical area to fail. Novel approaches are required to assess the network vulnerability under such regional failures. In this paper, we investigate the vulnerability of networks by considering the geometric properties of regional failures and network nodes. To evaluate the criticality of node locations and determine the critical areas in a network, we propose the concept of ${\alpha}$-critical-distance with a given failure impact ratio ${\alpha}$, and we formulate two optimization problems based on the concept. By analyzing the geometric properties of the problems, we show that although finding critical nodes or links in a pure graph is a NP-complete problem, the problem of finding critical areas has polynomial time complexity. We propose two algorithms to deal with these problems and analyze their time complexities. Using real city-level Internet topology data, we conducted experiments to compute the ${\alpha}$-critical-distances for different networks. The computational results demonstrate the differences in vulnerability of different networks. The results also indicate that the critical area of a network can be estimated by limiting failure centers on the locations of network nodes. Additionally, we find that with the same impact ratio ${\alpha}$, the topologies examined have larger ${\alpha}$-critical-distances when the network performance is measured using the giant component size instead of the other two metrics. Similar results are obtained when the network performance is measured using the average two terminal reliability and the network efficiency, although computation of the former entails less time complexity than that of the latter.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.963-973
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• 2005

Increasing attention is being recently devoted to various problems inherent in the topological design of networks systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cable. Lately, these network systems are well designed with tiber optic cable, because the requirements from users become increased. But considering the high cost of the fiber optic cable, it is more desirable that the network architecture is composed of a spanning tree. In this paper, we present a GA (Genetic Algorithm) for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable, considering the connection cost, average message delay, and the network reliability We also employ the $Pr\ddot{u}fer$ number (PN) and cluster string in order to represent chromosomes. Finally, we get some experiments in order to certify that the proposed GA is the more effective and efficient method in terms of the computation time as well as the Pareto optimality.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.471-480
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• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).