Earticle

In the recent years, non-preemptive job shop scheduling problems have been applied to a wide variety of academic and industrial fields. In comparison, preemptive job shop scheduling problems have received almost no attention in the both fields. Motivated by the needs of a specific application, we presented an algorithm for dealing with preemptive job shop scheduling problem. First, we considered constraint programming techniques to preemptive scheduling problems. Second, we applied genetic algorithm to these problems. In proposed genetic algorithm, we developed a new concept for representing of genetic algorithm. In case study, we applied the proposed algorithm to several job shop problems. Experiment results show that the proposed algorithm considered by preemptive problems outperforms non-preemptive case and other conventional algorithms.

Altshuller와 그의 동료들은 창의적이고 혁신적인 발명 특허들이 공통적으로 모순을 해결하였다는 사실을 알아내어 트리즈(TRIZ)를 정립하 였다. TRIZ는 ‘발명 문제 해결 이론(Theory of Inventive ProblemSolving)’을 의미하는 러시아어의 머리글자에 해당한다. 국내외 유수 기업들이 혁신적인 신상품 개발을 위해 TRIZ를 적극적으로 도입하고 있다. TRIZ는 문제의 모순을 해결하는 방법으로써 분리원리와 40 발명원리 등을 제 시한다. 그렇지만 초보자가 TRIZ를 활용하기에는 TRIZ의 내용이 방대하고 복잡하여 실제적 활용에 많은 어려움을 겪는다. 이에 본 연구는 대중 적으로 널리 알려진 키플링의 육하원칙 방법과 가치공학의 도구-대상 분석을 활용하여 모순문제를 정의하고 문제의 해결방안을 도출하는 알고 리즘을 제시하였다. 키플링의 육하원칙 방법을 적용하면 문제의 목표와 해결수단 그리고 문제가 발생하는 시공간을 분석할 수 있는 이점이 있 다. 키플링의 육하원칙 방법을 적용하는 과정에서 모순문제의 목표(Why), 시스템의 추상적인 목표를 구체적으로 달성하기 위한 무엇(What), 시 스템의 목표를 이루기 위한 수단으로써 어떻게(How)를 파악할 수 있다. 이후 적용하는 도구-대상 분석은 시스템의 구성요소들이 어떻게 상호작 용하는지 파악할 수 있다. 키플링 육하원칙 방법과 도구-대상 분석의 연계 과정은 모순문제의 추상적인 목표와 구체적인 해결수단 간의 구조적 인 관계를 정의하게 하며, 이를 시간-도구 테이블에 적용하여 문제 해결을 위한 구체적인 해결안을 도출할 수 있다.

Altshuller and his colleagues found that creative and innovative invention patents commonly resolved contradictions and established TRIZ. TRIZ is an acronym in Russian meaning ‘Theory of Inventive Problem Solving’. Leading domestic and foreign companies are actively introducing TRIZ to develop innovative new products. TRIZ proposes the separation principle and 40 invention principles as a way to solve the contradiction of the problem. For beginners to use TRIZ, the contents of TRIZ are vast and complex, so it is difficult for beginners to actually use TRIZ. This study presents an algorithm that defines the contradiction problem and derives a solution to the problem using the widely known Kipling's six-fold principle method, the tool-object analysis of value engineering that defines the contradiction problem. Applying Kipling's six-fold principle method has the advantage of analyzing the goal, solution, and time and space in which the problem occurs. In the process of applying Kipling's six-fold principle method, the goal of the contradiction problem(Why), what to achieve concretely the abstract goal of the system, and how as a means to achieve the goal of the system can be grasped. After applying Kipling's six-fold principle method, tool-object analysis can be used to understand how the components of a systeminteract. By understanding the components of the systemand their interactions, it can be extracted the definition the structural relationship between the abstract goal of the contradiction problem and the concrete solution by these analyses. After they are done, a specific solution can be derived for solving the problem by using the time-tool table.

컴퓨터과학의 원리를 교육의 기반이 되는 알고리즘 교육이 초등학교에서부터 강조되고 있다. 그러나 초등학생에 적합한 알고리즘을 교육시키고 초등학생들이 이해하기는 어렵다. 본 연구에서는 초등학생들이 알고리즘에 대해 쉽게 배울 수 있도록 검증되어진 컴퓨터과학 언플러그드의 프로그램을 기반으로 교육과정 및 학습자료를 개발하였다. 학습방법은 활동중심학습으로 구성 하였으며, 학습내용은 해싱기법을 사용하여 주어진 자료를 정렬하고, 요구되는 자료를 찾는 교육과정을 제시하였다. 본 연구에서 제시한 교육과정과 학습자료는 전문가 집단의 검증 및 정보담당 선생님의 설문 분석을 통해 적절하다는 결론을 얻었다.

Algorithm education that become at the base of computational thinking is emphasized as an instrument for teaching the basic principles of Computer Science. We’ll present ‘the sorting teaching contents for algorithm in the elementary student. And they will successfully guide the student to understand sorting using algorithm. The activity-based learning is provided for the contents, and the data will be found out in everyday life. To check the adequacy of these materials they were tested to the elementary classroom, and the results can help to enhance the problem solving ability and the creativity.

가까운 미래에 인공지능과 컴퓨터 네트워크 기술이 발전함에 따라, 인공지능과의 협업이 중요하게 될 것이다. 인공 지능 시대에는 사람 간의 의사소통과 협업 능력이 인재의 중요한 요소라고 할 수 있다. 이를 위해서, 컴퓨터 과학 기반의 인공지능이 어떻게 동작하는지를 파악하는 것이 필요하다. 컴퓨터 과학 교육을 위해서는 문제 해결 학습 중심의 알고리즘 교육에 초점을 두는 것이 효율적이다. 본 연구에서는 문제 해결 학습 중심의 알고리즘 교육을 받은 대학생 28명을 대상으 로 학기 초의 컴퓨팅 사고력 진단을 실시한 결과와 학기 말의 만족도 조사와 학업 성적을 비교 분석하였다. 학생들의 컴퓨 팅 사고력을 진단한 결과와 문제 해결 학습, 교수법, 강의 만족도, 기타 환경 요인에서 상관관계가 나타났고, 회귀분석을 실시한 결과 문제 해결 학습이 강의 만족도와 컴퓨팅 사고력 향상에 영향을 주었음을 확인하였다. 컴퓨터 과학 교육을 위해서 문제 해결 학습 기법과 함께 학생들의 만족도를 향상하는 방법을 추구한다면 학생들의 문제 해결 능력 향상에 도움이 될 것이다.

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. An algorithmic education focused on problem solving and learning is efficient for computer science education. In this study, the results of an assessment of computational thinking at the beginning of the semester, a satisfaction survey at the end of the semester, and academic performance were compared and analyzed for 28 students who received algorithmic education focused on problem-solving learning. As a result of diagnosing students’ computational thinking and problem-solving learning, teaching methods, lecture satisfaction, and other environmental factors, a correlation was found, and regression analysis confirmed that problem-solving learning had an effect on improving lecture satisfaction and computational thinking ability. For algorithmic education, if you pursue a problem-solving learning technique and a way to improve students’ satisfaction, it will help students improve their problem-solving skills.

프로그래밍의 핵심은 어디까지나 알고리즘 학습에 있으며 이를 통한 창의적이고 논리적인 문제해결력의 향상이 프로그래밍 학습의 목표인 것이다. 그렇다면 어떤 알고리즘들을 어떠한 순서대로 가르치는가에 대한 고민을 좀 더 해 볼 필요가 있으며 그 효과성에 대해서도 연구해 볼 필요가 있을 것이다. 본 연구는 개념적 알고리즘의 내용들을 한국정보올림피아드 초등부 경시부문의 문제들을 이용하여 학습할 수 있도록 알고리즘 학습 교재를 개발하고 이 효과를 검증하였다.

The core of programming learning is based on an algorithm learning and the promotion of problem solving abilities is the purpose of this learning. Then, we need to think about what kind of algorithms in what order we teach and we need to study the effect of this learning. The purpose of this study is development and implementation of algorithm instructional materials and examine the effect of an algorithm learning with conceptual algorithms in KOI(Korea Olympiad in Informatics) final test of elementary students.

현대는 소프트웨어 융합의 시대이다. 4차 산업혁명이라고 불리우는 이러한 변화가 우리 삶의 거의 모든 분야에 영향을 미침에 따라 초․중등 교육의 내용도 이러한 변화를 수용해야 한다는 요구를 반영하여 2015 개정 교육과정에서 소프트웨어 교육이 강화되었다. 따라서, 본 연구는 초등 소프트웨어 교육을 위하여 플립러닝을 위한 STEAM 기반 초등 알고리즘 학습용 모바일 웹앱을 활용한 교육이 초등학생의 문제해결과정에 미치는 영향을 분석하여 그 효과성을 검증하는 데 목적이 있다. 이를 위해 본 연구에서는 새로운 교육과정을 분석하고, 초등학생의 플립러닝에 기여할 수 있도록 모바일 기기를 교육에 응용하여 IT 융합시대에 적합한 STEAM 기반 모바일 콘텐츠를 개발하였다. 그리고 개발한 모바일 자료를 초등학교 5학년 대상으로 모바일 기기를 활용하여 플립러닝 수업을 진행한 실험집단에 적용하였고, 활동지를 활용하여 강의식 수업을 진행한 통제집단과 문제해결과정을 비교할 수 있는 통계적 t-검증을 실시하였다. 그 결과 실험집단과 통제집단 간의 문제해결과정에 통계적으로 유의미한 차이가 있음이 검증되었다. 따라서, 본 연구결과를 토대로 STEAM 기반 모바일 학습자료 활용 교육이 초등학생의 문제해결력 향상에 효과적임을 확인하였다.

Software integration becomes very important in these days. Since the 4th industrial revolution has begun and influences its heavy effects on our daily life, software education has been introduced in the 2015 national revised curriculum. Therefore, the purpose of this study is to verify the effects of a mobile web application for the elementary algorithm class based on STEAM on the problem solving process of elementary school students. To do so, in this study we analyzed the new elementary school curriculum, constructed an algorithm learning class based on STEAM and developed a mobile web application for flipped learning to improve their problem solving ability. Further, an experimental group and a controlled group are selected respectively from the 5th grade elementary school students. Then, a new flipped learning class using our mobile materials was applied to the experimental group while a traditional lecture class using the activity papers was applied to the controlled group. Finally the paired samples t-tests were carried out. As a result, we found that there was a statistically significant difference in problem solving process between the two groups. Based on our experimental research and the results of statistical analysis, the mobile web application class based on STEAM turned out to be effective in improving the problem solving ability of elementary school students.

Conception and calculation method of information entropy is customized for Sudoku puzzles and a corresponding algorithm is designed to solve Sudoku puzzles. The definitions of inverse information entropy and information amount for inverse information entropy are also introduced and directly used instead of information entropy in order to simplify the solving procedure. Experimental results show that the algorithm has better time efficiency than available methods including generic algorithms and rule based algorithms and it can solve not only unique-solution puzzles (including extremely difficult puzzles) but also multiple-solution puzzles. It is concluded that information entropy can be used for grading Sudoku puzzles as well.

Among creative and innovative problem solving algorithms, TRIZ solves difficult problems by finding contradictions of the problems. In TRIZ, there are two types of contradictions. One is a technical contradiction and the other is physial contradiction. A technical contradiction occurs between two desirable functions of a system. We call it a trade-off contradiction. On the other hand, a physical contradiction appears when two opposite properties are required from the same part of a system. In TRIZ, the ARIZ has been developed by Altshuller for reducing trial-and-error while solving problems. It is known as an inventive problem solving. It transforms difficult technical contradictions of a given problem into the corresponding physical contradiction to solve the problem easily. However, ARIZ-85c, the most recent version of the ARIZ, has inefficient and time-consuming features that cause trial-and-errors. In this paper, we propose the Butterfly algorithm based on the Butterfly diagram to reduce trial-and-error features by giving the right solution strategy based on propositional logic when selecting technical contradictions and physical contradictions for a given problem. The Butterfly algorithm can systematically find the solution strategy for the problem, and thus it helps to solve contradictions efficiently.

To improve the forecasting accuracy of oil prices, this paper has proposed oil price predicting model (PSR-LSSVM) based on unified solving by phase space reconstruction and predicting algorithm parameters using interrelation between phase-space reconstruction and predicting algorithm. The LSSVM is selected as the predicting algorithm of oil prices, and the parameters of phase space reconstruction and LSSVM are taken as individuals of the genetic algorithm, and the optimal delay time, embedding dimension and LSSVM parameters are obtained through selection, crossover and mutation evolutionary mechanism, and finally, the predicting model of oil prices is established and the performance of predicting model is tested by Daqing oil price time series. The results show that the proposed model PSR-LSSVM obtains higher predicting accuracy than the oil-price forecasting models of independently optimized phase-space reconstruction and LSSVM, which provides a new research idea for the predicting problem of chaotic time series.

This paper concerns the Optimal Power Flow [OPF] in multi machine Power System with UPFC using Genetic Algorithm [GA]. The objective is to minimize the cost of the power system, to enhance the power flow in transmission lines and to maintain the voltages at the buses using UPFC. Using the proposed method, the optimal cost and real power losses of the power system with UPFC is achieved by developing a simple Genetic Algorithm and the location and rating of UPFC is also achieved by Newton Raphson’s load flow method. IEEE 9 bus system has been studied to show the effectiveness.

Based on the the characteristics of maximal function, the algorithm for solving Min- max problem was researched in this paper. A new differential approximation function for the nondifferentiability of the objection function was also constructed. At same time, the property of the new differential approximation function is discussed and the processes of proves show that using the new approximation function to solve the nonlinear unconstrained min-max problem is feasible and effective. The preliminary numerical example shows that the algorithm is effective, and that have a large of convergence characteristics.

This paper considers the problem of designing a Multilayer Survivable Optical Network for the customers’ Demands Problem called MSONDP. The network is modelled by two graphs: an undirected graph G1 = (V1, E1) and a complete undirected and weighted graph G2 = (V2, E2, c). The goal objective of this problem is to design connections based on customers’ demands with the smallest a minimum network cost to protect the network against all failures. This paper introduces a multi-objective approach for MSONDP. These objectives are to minimize the network cost (totalCost) and the maximum number of connections passing over a link (maxConn). Further, this paper also proposes a multi-objective genetic algorithm to solve this problem. The eExperimental results on real world and random instances are reported to show the efficiencyefficacy, in terms of minimizing the network cost, of the proposed algorithm comparing compared to the single genetic PGAMSONDP.

The thesis mainly studies bi-criteria no-wait flexible flow shop problem, whose optimi-zation objective is to minimize the maximum completion time and the maximum delay time. This problem is NP hard, yet enjoying important theoretical research value, thereby this thesis proposes elite particle swarm optimization (EPSO) to solve bi-criteria no-wait flex-ible flow shop problem. EPSO algorithm applies five modified heuristic algorithms and random methods to generating initial population. Moreover, for the particle personal best, this thesis puts forward elite crossover algorithm, which retains continuous fragments of the identical workpieces among excellent individuals, avoiding the destruction of good continuity between solutions of workpieces. In addition, in order to avoid algorithm into local optimum, this thesis raises double insertion disturbance algorithm to help particles jump out the local optimal state and expand the feasible search range. For the purpose of effectively evaluating algorithm quality, there is a comparison among EPSO algorithm, PSO algorithm and ICA algorithm in simulation experiment that is respectively aimed at small-scale problem and large-scale scheduling problem, the results of which show that the proposed EPSO algorithm, due to better validity and accuracy, is superior to the PSO algorithm and ICA algorithm.

In order to improve the weak situation of the global search ability, the stability and time consuming of optimization of differential evolution(DE) algorithm in solving high dimensional optimization problem, an improved differential evolution algorithm with multi- population and multi-strategy(MPMSIDE) is proposed to solve high dimensional optimization problem. Firstly, the different DE mutation strategies are studied. Then the MPMSIDE algorithm divides the population into several sub-populations, which evolve independently and communicate with each other at regular intervals by using different DE strategies, in order to save the computation time. And the improved mutation strategy and local optimization strategy are introduced to raise and balance the global searching ability and local searching ability, and improve the optimization efficiency. The selfadaptive update strategy is used to adjust the scaling factor and crossover factor for making the parameter sensitivity of DE algorithm and improving the stability and robustness. Finally, the proposed MPMSIDE algorithm is applied to standard test function optimization for verifying the effectiveness. The experimental results show that the proposed MPMSIDE algorithm has a relatively better optimization performance for solving complex optimization problem, and takes on remarkable optimizing ability, higher searching accuracy and faster convergence speed.

The basic ant colony optimization (ACO) algorithm takes on a longer computing time in the search process and is prone to fall into local optimal solutions, an improved ACO (CEULACO) algorithm is proposed in this paper. In the CEULAC algorithm, the direction guidance and real variable function are used to initialize pheromone concentration according to the path information of undirected graph. The pheromone dynamic evaporation rate strategy is proposed to control the pheromone evaporation in order to increase the global search capability and accelerate the convergence speed. An adaptive dynamic factor is introduced into pheromone updating rule to control the updating proportion of pheromone concentration with optimal solution in single iteration. And the local search strategy is used to improve the quality of the solution and select the current optimal path for global updating the pheromone in order to save some computing time and not reduce the searching efficiency. Some traveling salesman problems are selected to test the performance of the CEULACO algorithm. The simulation experiments show that the improved ACO algorithm can effectively improve the accuracy and the quality of solutions, and distinctly speed up the convergence speed and computing time.

This paper presents a double mutation cuckoo search algorithm (DMCS) to overcome the disadvantages of traditional cuckoo search algorithms, such as bad accuracy, low convergence rate, and easiness to fall into local optimal value. The algorithm mutates optimal fitness parasitic nests using small probability, which enhances the local search range of the optimal solution and improves the search accuracy. Meanwhile, the algorithm uses large probability to mutate parasitic nests in poor situation, which enlarges the search space and benefits the global convergence. The experimental results show that the algorithms can effectively improve the convergence speed and optimization accuracy when applied to basic test functions and systems of nonlinear equations.

For solving traveling salesman problem (TSP), the ant colony optimization (ACO) algorithm and simulated annealing (SA) algorithm are used to propose a two-phase hybrid optimization (TPASHO) algorithm in this paper. In proposed TPASHO algorithm, the advantages of parallel, collaborative and positive feedback of the ACO algorithm are used to implement the global search in the current temperature. And adaptive adjustment threshold strategy is used to improve the space exploration and balance the local exploitation. When the calculation process of the ACO algorithm falls into the stagnation, the SA algorithm is used to get a local optimal solution. And the obtained best solution of the ACO algorithm is regarded as the initial solution of the SA algorithm, and then a fine search is realized in the neighborhood. Finally, the probabilistic jumping property of the SA algorithm is used to effectively avoid falling into local optimal solution. In order to verify the effectiveness and efficiency of the proposed TPASHO algorithm, some typical TSP is selected to test. The simulation results show that the proposed TPASHO algorithm can effectively obtain the global optimal solution and avoid the stagnation phenomena. And it has the better search precision and the faster convergence speed.

In order to improve the global searching ability of differential evolution algorithm in solving complex optimization problem, an improved differential evolution (SMDE) algorithm based on the self-adaptive method and multi-population is proposed in this paper. In the proposed SMDE algorithm, the population is divided into multi-populations in order to keep the diversity, then the self-adaptive method is used to control the parameters of differential evolution algorithm in order to balance the local search and global search ability. Finally, several complex benchmark functions are selected to validate the efficiency of the SMDE algorithm. The experiment results show that the proposed SMDE algorithm is better at the global convergence ability and the searching precision.

Using ant colony algorithm to solve TSP (traveling salesman problem) has some disadvantages as easily plunging into local minimum, slow convergence speed and so on. In order to find the optimal path accurately and rapidly, an improved ant colony algorithm is proposed. Experimental results show that the improved ant colony algorithm has better effectiveness for TSP problems solutions.

Particle swarm optimization (PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. It is a simple and efficient optimization algorithm. But it exists the low computational speed and easy falling into local optimal solution in solving the complex problem. So the quantum theory, adaptive inertia weight, disturbance factor and diversity mutation strategy are introduced into the PSO algorithm in order to propose an improved PSO(IWDMDQPSO) algorithm in this paper. In the IWDMDQPSO algorithm, the quantum theory is used to change the updating mode of the particles for guaranteeing the simplification and effectiveness of the algorithm. The adaptive inertia weight is used to improve the premature convergence of the algorithm. The disturbance factor is used to avoid the premature of the algorithm. The diversity mutation strategy is used to improve the global searching ability and computation speed. Finally, the famous benchmark functions are selected to prove the performance and effectiveness of the proposed IWDMDQPSO algorithm. The experiment results show that the proposed IWDMDQPSO algorithm takes on better solving accuracy and higher computation speed in solving the complex function. So it has a remarkable optimization performance.