• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.28 no.3
• /
• pp.149-155
• /
• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Journal of the Korea Safety Management & Science
• /
• v.10 no.3
• /
• pp.217-225
• /
• 2008

The inventory routing problem (IRP) is an important area of Supply Chain Management. The objective function of IRP is the sum of transportation cost and inventory cost. We propose an Artificial Immune System(AIS) to solve the IRP. AIS is one of natural computing algorithm. An hyper mutation and an vaccine operator are introduced in our research. Computation results show that the hyper mutation is useful to improve the solution quality and the vaccine is useful to reduce the calculation time.

• Journal of applied mathematics & informatics
• /
• v.30 no.1_2
• /
• pp.183-200
• /
• 2012

A two-strain epidemic model with an age structure mutation and varying population is studied. By means of the spectrum theory of bounded linear operator in functional analysis, the reproductive numbers according to the strains, which associates with the growth rate ${\lambda}^*$ of total population size are obtained. The asymptotic stability of the steady states are obtained under some sufficient conditions.

• Journal of Korean Institute of Industrial Engineers
• /
• v.25 no.4
• /
• pp.490-499
• /
• 1999

Vehicle routing problem(VRP) is known to be NP-hard problem, and good heuristic algorithm needs to be developed. To develop a heuristic algorithm for the VRP, this study suggests a parallel genetic algorithm(PGA), which determines each vehicle route in order to minimize the transportation costs. The PGA developed in this study uses two dimensional array chromosomes, which rows represent each vehicle route. The PGA uses new genetic operators. New mutation operator is composed of internal and external operators. internal mutation swaps customer locations within a vehicle routing, and external mutation swaps customer locations between vehicles. Ten problems were solved using this algorithm and showed good results in a relatively short time.

• The KIPS Transactions:PartD
• /
• v.16D no.4
• /
• pp.517-526
• /
• 2009

It is possible to indicate the complex design and execution of object-oriented program with dynamic UML diagram. This paper shows the way how to make several test cases from sequence, state, and activity diagram among dynamic UML diagram. Three dynamic UML diagrams about withdrawal work of ATM simulation program are drawn. Then different test cases are created from these diagrams using previously described ways. To evaluate effectiveness of test cases, mutation testing is executed. Mutants are made from MuClipse plug-in tool based on Eclipse which supports many traditional and class mutation operators. Finally we've got the result of mutation testing and compare effectiveness of test cases, etc. Through this document, we've known some hints that how to choose the way of making test cases.

• Journal of Electrical Engineering and Technology
• /
• v.2 no.4
• /
• pp.413-419
• /
• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Journal of Microbiology and Biotechnology
• /
• v.12 no.4
• /
• pp.622-627
• /
• 2002

Diphtheria toxin repressor (DtxR) binds to approximately 30 to 35-bp regions containing an interrupted 9-bp inverted repeat within a 19-bp core sequence. The core sequence is fairly conserved and critical for DtxR binding. The flanking regions that are consisted of 5 to 8 more of nucleotides from the core are also required for DtxR binding. The nucleotides in both flanking regions are A-T rich. To examine whether the A-T nucleotides in both flanking regions from the core have significant roles for DtxR binding, a DNA fragment was constructed based on the diphtheria tox promoter/operator, and DNA fragments with substitution of A and T nucleotides In the flanking regions to G and C were also constructed. To assess the effect of these substitutions on binding of DtxR and repressibility by DtxR, $\beta$-galactosidase activity from lacZ fused to the region was assessed. Gel mobility shift of the region by purified DtxR was also examined. The DNA fragments containing the mutations in the flanking regions still exhibited repression and mobility shift with DtxR. The core segment with the mutation is still, therefore, recognized by DtxR. Nonetheless, the results from the assays indicated that the substitution significantly decreased repression of the operator by DtxR in vivo under high-iron condition and decreased binding of DtxR to the operator. These results suggest that A and T nucleotides fur both flanking regions are preferred for the binding of DtxR.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.04a
• /
• pp.609-612
• /
• 2000

Job-shop scheduling problem(JSSP) is one of the best-known machine scheduling problems and essentially an ordering problem. A new encoding scheme which always give a feasible schedule is presented, by which a schedule directly corresponds to an assigned-operation ordering string. It is initialized with G&T algorithm and improved using the developed genetic operator; APMX or BPMX crossover operator and mutation operator. and the problem of infeasibility in genetic generation is naturally overcome. Within the framework of the newly designed genetic algorithm, the NP-hard classical job-shop scheduling problem can be efficiently solved with high quality. Moreover the optimal solutions of the famous benchmarks, the Fisher and Thompson's 10${\times}$10 and 20${\times}$5 problems, are found.

• Journal of KIISE
• /
• v.41 no.12
• /
• pp.1090-1098
• /
• 2014

In this paper, an improved ACDE (Adaptive Cauchy Differential Evolution) algorithm with faster convergence speed, called ACDE2, is suggested. The baseline ACDE algorithm uses a "DE/rand/1" mutation strategy to provide good population diversity, and it is appropriate for solving multimodal optimization problems. However, the convergence speed of the mutation strategy is slow, and it is therefore not suitable for solving unimodal optimization problems. The ACDE2 algorithm uses a "DE/current-to-best/1" mutation strategy in order to provide a fast convergence speed, where a control parameter initialization operator is used to avoid converging to local optimization. The operator is executed after every predefined number of generations or when every individual fails to evolve, which assigns a value with a high level of exploration property to the control parameter of each individual, providing additional population diversity. Our experimental results show that the ACDE2 algorithm performs better than some state-of-the-art DE algorithms, particularly in unimodal optimization problems.

• The KIPS Transactions:PartB
• /
• v.9B no.5
• /
• pp.673-680
• /
• 2002

This paper proposes a method for detecting edge using an evolutionary programming and a momentum back-propagation algorithm. The evolutionary programming does not perform crossover operation as to consider reduction of capability of algorithm and calculation cost, but uses selection operator and mutation operator. The momentum back-propagation algorithm uses assistant to weight of learning step when weight is changed at learning step. Because learning rate o is settled as less in last back-propagation algorithm the momentum back-propagation algorithm discard the problem that learning is slow as relative reduction because change rate of weight at each learning step. The method using EP-MBP is batter than GA-BP method in both learning time and detection rate and showed the decreasing learning time and effective edge detection, in consequence.