• Journal of KIISE:Computer Systems and Theory
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• v.32 no.8
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• pp.399-410
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• 2005

This paper Proposes a novel adaptive genetic algorithm (GA) extrapolated by an ant colony optimization. We first prove that the algorithm converges to the unique global optimal solution with probability arbitrarily close to one and then, by experimental studies, show that the algorithm converges faster to the optimal solution than GA with elitism and the population average fitness value also converges to the optimal fitness value. We further discuss controlling the tradeoff of exploration and exploitation by a parameter associated with the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.903-921
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• 2024

Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

• Steel and Composite Structures
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• v.25 no.1
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• pp.105-116
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• 2017

This paper presents the dynamic stability study of laminated composite plates with different force combinations and aspect ratios. Optimum non-diverging stacking is obtained for certain loading combination and aspect ratio. In addition, the stability force is maximized for a definite operating frequency. A dynamic version of the principle of virtual work for laminated composites is used to obtain force-frequency relation. Since dynamic stiffness governs the divergence or flutter, an efficient optimization method is necessary for the response functional and the relevant constraints. In this way, a model based on the ant colony optimization (ACO) algorithm is proposed to search for the proper stacking. The ACO algorithm is used since it treats with large number of dynamic stability parameters. Governing equations are formulated using classic laminate theory (CLT) and von-Karman plate technique. Load-frequency relations are explicitly obtained for fundamental and secondary flutter modes of simply supported composite plate with arbitrary aspect ratio, stacking and boundary load, which are used in optimization process. Obtained results are compared with the finite element method results for validity and accuracy convince. Results revealed that the optimum stacking with stable dynamic response and maximum critical load is in angle-ply mode with almost near-unidirectional fiber orientations for fundamental flutter mode. In addition, short plates behave better than long plates in combined axial-shear load case regarding stable oscillation. The interaction of uniaxial and shear forces intensifies the instability in long plates than short ones which needs low-angle layup orientations to provide required dynamic stiffness. However, a combination of angle-ply and cross-ply stacking with a near-square aspect ratio is appropriate for the composite plate regarding secondary flutter mode.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.95-102
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• 2012

In this paper, we propose a method that can construct efficiently a P2P overlay multicast network composed of many heterogeneous peers in communication bandwidth, processing power and a storage size by selecting a peer in a distributed fashion using an ant-colony theory that is one of the computational intelligence methods. The proposed method considers not only the capacity of a peer but also the number of children peers supported by the peer and the hop distance between a multicast source and the peer when selecting a parent peer of a newly joining node. Thus, an P2P multicast overlay network is constructed efficiently in that the distances between a multicast source and peers are maintained small. In addition, the proposed method works in a distributed fashion in that peers use their local information to find a parent node. Thus, compared to a centralized method where a centralized server maintains and controls the overlay construction process, the proposed method scales well. Through simulations, we show that, by making a few high capacity peers support a lot of low capacity peers, the proposed method can maintain the size of overlay network small even there are a few thousands of peers in the network.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.133-149
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• 2017

As cities face increasing problems such as aging, environmental pollution and growth limits, we have been trying to incorporate sustainability into urban planning and related policies. However, it is very difficult to generate a 'sustainable spatial plans' because there are trade-offs among environmental, society, and economic values. This is a kind of non-linear problem, and has limitations to be solved by existing qualitative expert knowledge. Many researches from abroad have used the meta heuristic optimization algorithms such as Genetic Algorithms(GAs), Simulated Annealing(SA), Ant Colony Optimization(ACO) and so on to synthesize competing values in spaces. GAs is the most frequently applied theory and have been known to produce 'good-enough plans' in a reasonable time. Therefore we collected the research on 'spatial optimization model based GAs' and analyzed in terms of 'study area', 'optimization objective', 'fitness function', and 'effectiveness/efficiency'. We expect the results of this study can suggest that 'what problems the spatial optimization model can be applied to' and 'linkage possibility with existing planning methodology'.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.147-160
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• 2010

The system virtualization shows interest in the consolidation of servers for the efficient utilization of system resources. There are many various researches to utilize a server machine more efficiently through the system virtualization technique, and improve performance of the virtualization software. These researches have studied with the activity to control the resource allocation of virtual machines dynamically focused on CPU, or to manage resources in the cross-machine using the migration. However, the researches of the memory management have been wholly lacking. In this respect, the use of memory is limited to allocate the memory statically to virtual machine in server consolidation. Unfortunately, the static allocation of the memory causes a great quantity of the idle memory and decreases the memory utilization. The underutilization of the memory makes other side effects such as the load of other system resources or the performance degradation of services in virtual machines. In this paper, we suggest the dynamic allocation of the memory in Xen to control the memory allocation of virtual machines for the utilization without the performance degradation. Using AR model for the prediction of the memory usage and ACO (Ant Colony Optimization) algorithm for optimizing the memory utilization, the system operates more virtual machines without the performance degradation of servers. Accordingly, we have obtained 1.4 times better utilization than the static allocation.