• Journal of Information Processing Systems
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• v.18 no.3
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• pp.389-401
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• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.201-206
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• 2006

Genetic Algorithms have been known to provide near optimal solutions for various optimization problems in engineering. In this paper, we study the effect of gene order in genetic algorithms on the defining length of the schema with high fitness values. Its effect on the performance of genetic algorithms was also analyzed through two well known problems. A few gene reordering methods were proposed for graph partitioning and knapsack problems. Experimental results showed that genetic algorithms with gene reordering could find solutions of better qualities compared to the ones without gene reordering. It is very important to find proper reordering method for a given problem to improve the performance of genetic algorithms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.10a
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• pp.35-35
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• 1994

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.3-13
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• 1993

In this study, we develop a B & B type algorithm for the concave minimization problem with 0-1 knapsack constraint. Our algorithm reformulates the original problem into the singly linearly constrained concave minimization problem by relaxing 0-1 integer constraint in order to get a lower bound. But this relaxed problem is the concave minimization problem known as NP-hard. Thus the linear function that underestimates the concave objective function over the given domain set is introduced. The introduction of this function bears the following important meanings. Firstly, we can efficiently calculate the lower bound of the optimal object value using the conventional convex optimization methods. Secondly, the above linear function like the concave objective function generates the vertices of the relaxed solution set of the subproblem, which is used to update the upper bound. The fact that the linear underestimating function is uniquely determined over a given simplex enables us to fix underestimating function by considering the simplex containing the relaxed solution set. The initial containing simplex that is the intersection of the linear constraint and the nonnegative orthant is sequentially partitioned into the subsimplices which are related to subproblems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2661-2667
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• 2013

Recently, at Home and abroad, Internet of Things era things(Thing) is participating as a subject of communication in human communication paradigm of existing (lot/M2M) is in full swing. Automobile, refrigerator, bicycle, until shoes, and communication functions generation of information is installed and has created a fusion of new service IT infrastructure. Its use and application are broadening to various areas and the number of devices used for it is increasing to increase the number of information transmitted for each object. When the traffic reaches its limit while each set of data is transmitted from the devices divided into each group through the mobile network, M2M communications service might not be processed smoothly. This study used the Knapsack Problem algorithm to create a virtual simulator for a smooth M2M service when the mobile network used for the M2M communications reaches its limit. The virtual simulator applies smooth processing of services from the M2M communications that should be processed first to other subsequent services when data comes to each group of devices. As the M2M technology develops to make many objects more compact in size, it would help with smoother processing of M2M services for the mobile network with fast-increasing traffic.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.17-24
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• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.411-416
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• 2007

The General security method of wireless network provides a confidentiality of communication contents based on the cryptographic stability against a malicious host. However, this method exposes the logical and physical addresses of both sender and receiver, so transmission volume and identification of both may be exposed although concealing that content. Covered address scheme that this paper proposes generates an address to which knapsack problem using super increasing sequence is applied, and replaces the addresses of sender and receiver with addresses from super increasing sequence. Also, proposed method changes frequently secret addresses, so a malicious user cannot watch a target system or try to attack the specific host. Proposed method also changes continuously a host address that attacker takes aim at. Accordingly, an attacker who tries to use DDoS attack cannot decide the specific target system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.56-63
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• 2016

We consider a satellite mission scheduling problem, which is a promising problem in recent satellite industry. This problem has various considerations such as customer importance, due date, limited capacity of energy and memory, distance of the location of each mission, etc. Also we consider the objective of each satellite such as general purpose satellite, strategic mission and commercial satellite. And this problem can be modelled as a general knapsack problem, which is famous NP-hard problem, if the objective is defined as to maximize the total mission score performed. To solve this kind of problem, heuristic algorithm such as taboo and genetic algorithm are applied and their performance are acceptable in some extent. To propose more efficient algorithm than previous research, we applied a particle swarm optimization algorithm, which is the most promising method in optimization problem recently in this research. Owing to limitation of current study in obtaining real information and several assumptions, we generated 200 satellite missions with required information for each mission. Based on generated information, we compared the results by our approach algorithm with those of CPLEX. This comparison shows that our proposed approach give us almost accurate results as just less than 3% error rate, and computation time is just a little to be applied to real problem. Also this algorithm has enough scalability by innate characteristic of PSO. We also applied it to mission scheduling problem of various class of satellite. The results are quite reasonable enough to conclude that our proposed algorithm may work in satellite mission scheduling problem.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.13-22
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• 1997

In this paper, we supposed allocating the number of redundancies as the model of 0-1 knapsack problem and formulated the problem to maximize the systems reliability for a mission length. The formulated problem reduced the problem size using the modified branch and bound algorithm by Lagrangian relaxation. The subgradient method can optimize the set of solution. To verify the proposed method, we presented the improved resutls of the systems composed of two and ten component groups as the commparison of those in other papers.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1550-1556
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• 1997

In this paper, we apply the evolutionary algorithm to the combinatorial optimization problem. Evolutionary algorithm useful for the optimization of the large space problem. This paper propose a method for the reuse of wastes of light water in atomic reactor system. These wastes contain several reusable elements, and they should be carefully selected and blended to satisfy requirements as an input material to the heavy water atomic reactor system. This problem belongs to an NP-hard like the 0/1 knapsack problem. Two evolutionary strategies are used as approximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method which perform the feasible test and solution evaluation by using the vectored knowledge in problem domain. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.