• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.427-436
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• 2010

This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.1
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• pp.33-49
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• 1995

Simulated annealing(SA) has been admitted as a general purpose optimization technique which can be utilized for almost all kinds of combinatorial optimization problems without much difficulty. But there are still some weak points to be resolved, one of which is the slow speed of convergence. In this study, we carefully review various previous efforts to improve SA and propose some variations of SA which can enhance the speed of convergence to the optimum solution. Then, we apply the revised SA algorithms to the scheduling and hardware allocation problems occurring in high-level synthesis(HLS) of VLSI design. We confirm the efficiency of the proposed methods through several HLS examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.21-28
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• 2005

This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.8
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• pp.122-129
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• 1997

In this paper, we propose a Hopfield model for solving the part-matching in case that is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and total path of part-connections. Therefore, this kind of problem is referred to as a combinatiorial optimization problem. First of all, we review the theoretical basis for Hopfield model and present two optimal algorithms of part-matching. The first algorithm is Traveling Salesman Problem(TSP) which improved the original and the second algorithm is Wdighted Matching Problem (WMP). Finally, we show demonstration through com- puter simulation and analyze the stability and feasibility of the generated solutions for the proposed con- nection methods. Therefore, we prove that the second algorithm is better than the first algorithm.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.195-202
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• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.5
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• pp.1183-1192
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• 1993

Since the punching time is strongly related to the productivity in sheet metal stamping, there have been a lot of efforts to obtain the optimal tool path. However, most of the conventional efforts have the basic limitations to provide the global optimal solution because of the inherent difficulties of the NP hard combinatorial optimization problem. The existing methods search the optimal tool path with limiting tool changes to the minimal number, which proves not to be a global optimal solution. In this work, the turret rotation time is also considered in addition to the bed translation time of the NCT machine, and the total punching time is minimized by the simulated annealing algorithm. Some manufacturing constraints in punching sequences such as punching priority constraint and punching accuracy constraint are incorporated automatically in optimization, while several user-interactions to edit the final tool path are usually required in commercial systems.

• Journal of the Korean Society of Marine Environment & Safety
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• v.1 no.2
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• pp.69-82
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• 1995

This study is concerned with developing a heuristic algorithm for solving a class of ninlinear integer programs(NLIP). Exact algrithm for solving a NLIP either may not exist, or may take an unrealistically large amount of computing time. This study develops a new heuristic, the Excursion Algorithm(EA), for solving a class of NLIP's. It turns out that excursions over a bounded feasible and/or infeasible region is effective in alleviation the risks of being trapped at a lical optimum. The developed EA is applied to the redundancy optimization problems for improving the system safety, and is compared with other existing heuristic methods. We also include simulated annealing(SA) method in the comparision experiment due to ist populatrity for solving complex combinatorial problems. Computational results indicate that the proposed EA performs consistently better than the other in terms of solution quality, with moderate increase in computing time. Therefore, the proposed EA is believed to be an attractive alternative to other heuristic methods.

• Earthquakes and Structures
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• v.7 no.6
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• pp.895-908
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• 2014

The main objective of critical excitation methods is to reveal the worst possible response of structures. This goal is accomplished by considering the uncertainties of ground motion, which is subjected to the appropriate constraints, such as earthquake power and intensity limit. The concentration of this current study is on the theoretical optimization aspect, as is the case with the majority of conventional critical excitation methods. However, these previous studies on critical excitation lead to a discontinuous power spectral density (PSD). This paper introduces some critical excitations which contain proper continuity in frequency domain. The main idea for generating such continuous excitations stems from the combination of two continuous functions. On the other hand, in order to provide a non-stationary model, this paper attempts to present an appropriate envelope function, which unlike the previous envelope functions, can properly cover the natural earthquakes' accelerograms based on multi-peak conditions. Finally, the proposed method is developed into the multiple-degree-of-freedom (M.D.O.F) structures.