• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.629-648
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• 2012

Given a video stream delivering system deployed on a multicast tree, which is embedded in a multi-channel multi-radio wireless mesh network, our problem is concerned about how to allocate interference-free channels to tree links and maximize the number of serviced mesh clients at the same time. In this paper, we propose a channel allocation heuristic algorithm based on best-first search and backtracking techniques. The experimental results show that our BFB based CA algorithm outperforms previous methods such as DFS and BFS based CA methods. This superiority is due to the backtracking technique used in BFB approach. It allows previous channel-allocated links to have feasibility to select the other eligible channels when no conflict-free channel can be found for the current link during the CA process. In addition to that, we also propose a tree refinement method to enhance the quality of channel-allocated trees by adding uncovered destinations at the cost of deletion of some covered destinations. Our aim of this refinement is to increase the number of serviced mesh clients. According to our simulation results, it is proved to be an effective method for improving multicast trees produced by BFB, BFS and DFS CA algorithms.

• Journal of Convergence Society for SMB
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• v.3 no.2
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• pp.19-25
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• 2013

Decision procedure was done with the evaluation of multi-criterion analysis. Importance of each criterion was considered through heuristically method, specially it was based on the heuristic least mean square algorithm. To consider coalition evaluation, it was carried out by calculation of Shapley index and Interaction value. The model output is also analyzed with the help of those two indexes, and the procedure was also displayed with details. Finally, the differences between the model output and the desired results are evaluated thoroughly, several problems are raised at the end of the example which require for further studying.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.473-481
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• 2015

In this study, we propose a clustering algorithm to enhance the performance of wireless sensor and actuator networks (WSANs). In each cluster, a multi-level hierarchical structure can be applied to reduce energy consumption. In addition to the cluster head, some nodes can be selected as intermediate nodes (INs). Each IN manages a subcluster that includes its neighbors. INs aggregate data from members in its subcluster, then send them to the cluster head. The selection of intermediate nodes aiming to optimize energy consumption can be considered high computational complexity mixed-integer linear programming. Therefore, a heuristic lowest energy path searching algorithm is proposed to reduce computational time. Moreover, a channel assignment scheme for subclusters is proposed to minimize interference between neighboring subclusters, thereby increasing aggregated throughput. Simulation results confirm that the proposed scheme can prolong network lifetime in WSANs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.265-278
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• 2021

When a dangerous event arises for people inside a building and an immediate evacuation is required, it is important that suitable routes have been previously defined. These situations can happen especially when buildings are crowded, making the occupants have a very high vulnerability and can be trapped if they do not evacuate quickly and safely. However, in most cases, routes are considered based just on their proximity or short distance to the exit areas, and evacuation simulations that include more variables are not performed. This work aims to propose a methodology for building's indoor evacuation activities under the premise of processing simulation scenarios in multi-agent environments. In the methodology, importance indexes of simplified and validated geometry data from a BIM (Building Information Modeling) are considered as heuristic input data in a proposed algorithm. The algorithm is based on AP-Theta^* pathfinding and collision avoidance machine learning techniques. It also includes conditioning variables such as the number of people, speed of movement as well as reaction ability of the agents that influence the evacuation times. Moreover, collision avoidance is applied between people or with objects along the route. The simulations using the proposed algorithm are tested in NetLogo for diverse scenarios, showing feasible evacuation routes and calculating evacuation times in a multi-agent environment. The experimental results are obtained by applying the method in a study case and demonstrate the level of effectiveness of the algorithm, and the influence of the conditioning variables analyzed together when performing safe evacuation routes.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.1
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• pp.13-25
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• 1999

This paper presents a model of competitive positioning and pricing of new products in a multi-segmented market. The segments in the market are located on a multi-dimensional discrete attribute space with fixed demands. Firms launch products sequentially on the attribute space, incurring fixed and variable costs, and then decide on their product prices. Each firm acts to maximize its profit. Market share of a firm is determined by the position and price of Its product. We provide sufficient conditions for the existence and uniqueness of Nash equilibrium Another equilibrium concept is Introduced and related to the Nash equilibrium. A heuristic algorithm based on genetic algorithms is designed to obtain the Nash equilibrium.

• Earthquakes and Structures
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• v.23 no.5
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• pp.419-430
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• 2022

The metallic energy dissipation device (EDD) has been widely accepted as a useful tool for passive control of buildings against earthquakes. The distribution of metallic EDDs in a multi-story building may have significant influence on its seismic performance, which can be greatly enhanced if the distribution scheme is properly designed. This paper addresses the optimal distribution problem in the aim of achieving a desired level of performance using the minimum number of metallic EDDs. Five local search heuristic algorithms are proposed to solve the problem. Four base structures are presented as numerical examples to verify the proposed algorithms. It is indicated that the performance of different algorithms may vary when applied in different situations. Based on the results of the numerical verification, the recommended guidelines are finally proposed for choosing the appropriate algorithm in different occasions.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Computers and Concrete
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• v.22 no.4
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

• Journal of Communications and Networks
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• v.9 no.2
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• pp.112-117
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• 2007

In this paper, we focus on the total transmission power minimization problem for downlink beamforming multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems while ensuring each user's QoS requirement. Although the linear integer programming (LIP) solution we formulate provides the performance upper bound of the margin adaptive (MA) optimization problem, it is hard to be implemented in practice due to its high computational complexity. By regarding each user's equivalent channel gain as approximate independent values and using iterative descent method, we present a heuristic MA resource allocation algorithm. Simulation results show that the proposed algorithm efficiently converges to the local optimum, which is very close to the performance of the optimal LIP solution. Compared with existing space division multiple access (SDMA) OFDM systems with or without adaptive resource allocation, the proposed algorithm achieves significant performance improvement by exploiting the frequency diversity and multi-user diversity in downlink multiple-input single-output (MISO) OFDM systems.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.171-181
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• 2008

Home care, known also as domiciliary care, is part of the community care service that is a responsibility of the local government authorities in the UK as well as many other countries around the world. The aim is to provide the care and support needed to assist people, particularly older people, people with physical or learning disabilities and people who need assistance due to illness to live as independently as possible in their own homes. It is performed primarily by care workers visiting clients' homes where they provide help with daily activities. This paper is concerned with the dispatching of care workers to clients in an efficient manner. The optimized routine for each care worker determines a schedule to achieve the minimum total cost (in terms of distance traveled) without violating the capacity and time window constraints. A collaborative population-based meta-heuristic called Particle Swarm Optimization (PSO) is applied to solve the problem. A particle is defined as a multi-dimensional point in space which represents the corresponding schedule for care workers and their clients. Each dimension of a particle represents a care activity and the corresponding, allocated care worker. The continuous position value of each dimension determines the care worker to be assigned and also the assignment priority. A heuristic assignment scheme is specially designed to transform the continuous position value to the discrete job schedule. This job schedule represents the potential feasible solution to the problem. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), insertion and swap, are embedded in the PSO algorithm in order to further improve the quality of the solution. The proposed methodology is implemented, tested, and compared with existing solutions for some 'real' problem instances.