Problem-solving Algorithm Research Articles

Conceptual approach to astronomical problems

New technical capabilities have brought about the sweeping growth of the amount of data acquired by the astronomers from observations with different instruments in various parts of the electromagnetic spectrum. We consider conceptual approach to be a promising tool to efficiently deal with these data. It uses problem domain knowledge to formulate the tasks and develop problem-solving algorithms and data analysis methods in terms of domain concepts without reference to particular data sources, and thereby allows solving certain problems in general form. We demonstrate the benefits of conceptual approach by using it to solve problems related to search for secondary photometric standard candidates, determination of galaxy redshifts, creation of a binary and multiple star repository based on inhomogeneous databases, and classification of eclipsing binaries.We formulate and solve these problems over specifications of astronomical knowledge units such as photometric systems, astronomical objects, multiple stars, etc., and define them in terms of the corresponding problem domains independently of the existing data resources.

Asymmetric cryptography and trapdoor one-way functions

The asymmetric-key (public-key) encryption scheme is considered to be the most important discovery in the history of cryptography. It is based on the use of two complementary keys generated according to a chosen trapdoor one-way function (TOWF). Since its first implementation, asymmetric encryption has revolutionized our way of communicating as well as the safety of information transfer, and it is now widely used around the world for various purposes, especially in the field of online transaction security. The safety of the asymmetric-key scheme relies on the assumption that any known cryptographic attack using an efficient problem-solving algorithm will not be able to succeed in applying the inverse (decryption) function onto the cryptogram in a polynomial time without additional knowledge (secret information). The most-challenging aspect of creating a new asymmetric cryptographic algorithm is selecting a one-way function for encryption purposes and finding a trapdoor in its inverse. In this paper, the concept of public-key cryptography will be explained using the RSA algorithm as an example. In addition, the review of the most-important functions that are considered to be trapdoor one-way functions will be conducted.

S. Malkin’s Algorithm of Inventive Problem Solving as an Instrument to Mastering Technical Systems in Supplementary Mathematical Education of School Students

The relevance of the research is determined by the complexity that secondary school students experience when they master the regularities of development and existence of technical systems as an important link in the creation of the structured knowledge system, abilities and the correct world perception, and training to search task solutions arising in technical systems. In this regard, the goal of the conducted research is to create approaches in teaching practices that will provide learner involvement in technical systems research and, consequently, will promote conditions for the solution of mathematical inventive problems. The main approach to the training of vocational technical creativity is G.S. Altshuller’s (1956) theory of inventive problem solving (TRIZ) that takes a specific place in Russia. Labor-intensiveness and complexity of application of TRIZ in the educational process determined the creation of simplified algorithms of inventive problem solving. . Malkin’s (2012) algorithm of inventive problem solving “Generator of ideas” was selected, which is based on a part of algorithms of inventive problem solving (ARIZ). The pilot study conducted since 2010 has helped to develop an approach to the organization of learners’ activities when working with the algorithm of inventive problem solving. Due to the approach that school students have formulated and solved tasks arising in technical systems, some solutions have been patented and certified by the Federal Service for Intellectual Property. The materials of this paper may be useful for teachers of general and supplementary mathematical education of school students, who are engaged in technical creativity, and for all those who study new opportunities of creative search in the sphere of mathematical apparatus of technical systems.

“Development of Thinking Skills” Course: Teaching TRIZ in Academic Setting

The question of “How to teach TRIZ” has been the subject of much discussion and debate. Bruner [1] argued that children should be encouraged to “treat a task as a problem for which one invents an answer, rather than finding one out there in a book or on the blackboard”. This statement was taken from Dehaan's [2] article, published in 2009, on “Teaching Creativity and Inventive Problem Solving in Science” (p. 174), in which Altshuller's “Theory of inventive problem solving” has not been mentioned. This demonstrates only one aspect of why teaching TRIZ in educational systems is a challenging mission.This paper describes the process and method used for developing the Development thinking skills and problem solving course in academic settings. The proposed program is a combination of a generic design and problem solving process thinking methods. The course was developed using 4 main thinking methodologies: Mayer's general problem description, Polya's algorithm for problem solving, Altshuller's Theory of inventive problem solving and System thinking. The new “Development thinking skills” course program is expected to overcome teaching and studying difficulties in acquiring advances problem solving skills in higher education setting.

Применение генетических алгоритмов для решения задач оптимизации системы организации и управления пассажирскими перевозками

Обоснованы преимущества и перспективы применения генетического алгоритма с целью оптимизации процесса организации и управления пассажирскими перевозками в условиях больших городов.

An adaptive stepsize algorithm for the numerical solving of initial-value problems

Abstract The present paper focuses on the efficient numerical solving of initial-value problems (IVPs) using digital computers and one-step numerical methods. We start from considering that the integration stepsize is the crucial factor in determining the number of calculations required and the amount of work involved to obtain the approximate values of the exact solution of a certain problem for a given set of points, within a prescribed computational accuracy, is proportional to the number of accomplished iterations. We perform an analysis of the local truncation error and we derive an adaptive stepsize algorithm which coupled with a certain one-step numerical method makes the use of this structure more computationally effective and insures that the estimated values of the exact solution are in agreement with an imposed accuracy. We conclude with numerical computations proving the efficiency of the proposed step selection algorithm.

Comparison of three unsupervised neural network models for first Painlevé Transcendent

In this paper, a reliable soft computing framework is presented for the approximate solution of initial value problem (IVP) of first Painleve equation using three unsupervised neural network models optimized with sequential quadratic programming (SQP). These mathematical models are constructed in the form of feed-forward architecture including log-sigmoid, radial base and tan-sigmoid activation functions in the hidden layers. The optimization of designed parameters for each model is performed with SQP, an efficient constraint optimization problem-solving algorithm. The designed methodology is tested on the IVP, and comparative study is carried out with standard solution based on numerical and analytical solvers. The accuracy, convergence and effectiveness of the schemes are validated on the given benchmark problem by large number of simulations and their comprehensive analysis.

Optimal Scheduling of Contract Algorithms for Anytime Problem-Solving

A contract algorithm is an algorithm which is given, as part of the input, a specified amount of allowable computation time. The algorithm must then complete its execution within the allotted time. An interruptible algorithm, in contrast, can be interrupted at an arbitrary point in time, at which point it must report its currently best solution. It is known that contract algorithms can simulate interruptible algorithms using iterative deepening techniques. This simulation is done at a penalty in the performance of the solution, as measured by the so-called acceleration ratio. In this paper we give matching (i.e., optimal) upper and lower bounds for the acceleration ratio under such a simulation. We assume the most general setting in which n problem instances must be solved by means of scheduling executions of contract algorithms in $m$ identical parallel processors. This resolves an open conjecture of Bernstein, Filkenstein, and Zilberstein who gave an optimal schedule under the restricted setting of round robin and length-increasing schedules, but whose optimality in the general unrestricted case remained open. Lastly, we show how to evaluate the average acceleration ratio of the class of exponential strategies in the setting of n problem instances and m parallel processors. This is a broad class of schedules that tend to be either optimal or near-optimal, for several variants of the basic problem.

Systematic Approach to Problem Solving of Low Quality Arc Welding during Pipeline Maintenance Using ARIZ (Algorithm of Inventive Problem Solving)

The pipelines in the oil and gas industry must be regularly checked for possible corrosion that might cause leakage of oil and gas. Magnetic flux leakage (MFL) technique is a magnetic method of nondestructive testing that is used to detect corrosion and metal loss in steel pipelines. The MFL device travels inside a pipeline to inspect the corrosion parts with a powerful magnet which is used to magnetize the pipe wall. At areas where there is corrosion or missing metal, the magnetic field leaks from the pipe wall and will be detected by magnetic sensors placed between the poles of the magnet and data is transmitted to the outside recording device for analysis. The problem occurs when it is necessary to remove the damaged segments of the pipe and replace them with the new ones by welding them to the existing pipelines. It is found that welding rod and arc column are subjected to some kind of force that causes them to deviate from the right position. In this paper, the problem of low quality arc welding during pipeline maintenance due to residual magnetic field on the pipeline caused by MFL inspection device is systematically analyzed to find the root causes and ideal solution of which internal resources are utilized, is attained by using ARIZ (in this paper, magnetic field from welding current is selected). Computer simulation using finite element method and field test are conducted and show satisfactory result for the proposed idea.

Solving non-convex economic dispatch problem via backtracking search algorithm

This paper presents BSA (backtracking search algorithm) for solving of ED (economic dispatch) problems (both convex and non-convex) with both the valve-point effects in the generator cost function and the transmission network loss considered. BSA is a new evolutionary algorithm for solving of numerical optimization problems; it uses a single control parameter and two crossover and mutation strategies for powerful exploration of the problem's search space. Four test systems (with 3, 6, 20, and 40 generators) are the case studies verifying the method's robustness and effectiveness. The results confirm that compared with existing well-known methods and especially in large-scale test systems, the proposed algorithm is the better approach to solving ED problems.

Dynamic Stability of Viscoelastic Flexible Plates of Variable Stiffness Under Axial Compression

The dynamic stability of viscoelastic plates of variable stiffness is analyzed. The deflections are described by partial integro-differential equations of motion. The Bubnov–Galerkin method based on monomial and polynomial approximation of deflections is used to reduce the problem to ordinary integro-differential equations with time as an independent variable. These equations are solved numerically using a singular kernel. A numerical problem-solving algorithm based on this method is described. A number of new mechanical effects are revealed

Application of The Agent-oriented Analysis to A Software System in Problem Cause Identification using Fuzzy Decision Tree

A problem cause identification software system is usually very complex and may contain multiroles dealing processes such as R&D, customer and manufacturing. The causes of a problem may have cognitive uncertainty properties, which need to be analyzed for the purpose of algorithm-based automatic problem-solving. In this study, an application of the scenario-driven agent-oriented analysis to a software system in problem cause identification is proposed. A case study is conducted to demonstrate the problem-solving algorithm for problem cause identification of a notebook computer product. The major contributions of this research include: (1) The proposal of an autonomous problem-solving system base on a scenario-driven agent-oriented approach. (2) The proposal of a complex system of problem cause identification based on the fuzzy data.

Systematic Approach to Arowana Gender Identification Problem using Algorithm of Inventive Problem Solving (ARIZ)

This paper presents a systematic approach to arowana gender identification problem using algorithm of inventive problem solving (ARIZ). Arowana is a beautiful and expensive fish. Many farms try to improve their efficiency in breeding arowana, but the problem is that arowana is monomorphic, which makes it difficult to distinguish male from female just from their appearance. This causes difficulties in mating and selling of arowana. Many trial-and-error methods have been used with little success. The problem of arowana gender identification cannot be solved easily with the psychological methods such as brainstorming or trial and error, it needs a more logical and well-structured method of problem solving. In this paper, ARIZ is used to systematically analyze the problem and search for possibilities to identify the gender of arowana step by step, and finally, among 32 ideas generated, some potential solutions have been evaluated and compared with the commonly used methods with satisfactory results which demonstrate the effectiveness of ARIZ as a powerful innovative problem solving tool.

English

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.

Innovative method by design-around concepts with integrating the algorithm for inventive problem solving

The work proposes a new design tool that integrates design-around concepts with the algorithm for inventive problem solving (Russian acronym: ARIZ). ARIZ includes a complete procedure for analyzing problems and related resource, resolving conflicts and generating solutions. The combination of ARIZ and design-around concepts and understanding identified principles that govern patent infringements can prevent patent infringements whenever designers innovate, greatly reducing the cost and time associated with the product design stage. The presented tool is developed from an engineering perspective rather than a legal perspective, and so can help designers easily to prevent patent infringements and succeed in innovating by designing around. An example is used to demonstrate the proposed method.

Oxidation kinetics of n-decane in the presence of an inhibiting composition: A mathematical model

We designed an algorithm and software for inverse kinetic problem solving by conditional global minimization of the deviation of calculated data from experimental data. This approach allows use of more complete information on the process, which narrows the region of feasibility and increases the quality of mathematical simulation of chemical reactions. The algorithms designed were tested in the kinetic description of a complex liquid-phase chain reaction, specifically, the oxidation of n-decane in the presence of an inhibiting composition, viz., para-oxydiphenylamine + n-decanol.

Eco-innovation by Integrating Biomimetic Design and ARIZ

This work presents a new eco-innovative biomimetic design tool by integrating the algorithm for inventive problem solving (Russian acronym: ARIZ) with biologically significant vocabularies to search for related biological cases. ARIZ includes a complete procedure for analyzing problem model and related resources, resolving conflicts and generating solutions. This tool searches for biological key words by using the Noun1-Noun2-Verb model in replace of the Su-Field model for analyzing system conflicts and finds related biological cases. Analyzing these biological cases and available resources helps designers to design eco-products based on biomimetic concepts. An example is used to demonstrate the capability of the proposed method.

GENERALIZED ALGORITHM OF COMBINE PROCESS ADJUSTMENT BASED ON FUZZY KNOWLEDGE MODELS

The problem of creating the mechanism of an expert system fuzzy inference meant for decision-making on the combine process adjustment is considered. The fuzzy inference algorithm is based on the domain models of ‘prior configuration’ and ‘process adjustment’. Fuzzification, composition, and defuzzification are general stages of the problem solution. The problem-solving mechanism is based on the deductive scheme (for the prior configuration problem), and on the inductive one (for the process adjustment problem). The specific feature of the proposed problem-solving algorithm is the hypothesis testing of emerging combining process non-conformances under the machine parameter variations. In this case, the validity of the exception condition generation when an additional breakdown in the technological process occurs is checked. The developed fuzzy inference algorithm and the domain model based on the fuzzy expert knowledge permit to approach considerably the solution to the decision-making automation problem under the combine process adjustment in the field environment.

An ARIZ-based life cycle engineering model for eco-design

Abstract Cleaner production issues require not only effective management and prevention but also a new design and engineering paradigm. This study presents an ARIZ-based life cycle engineering (LCE) model for implementing eco-designs of products. The aims of the proposed model are (1) to restructure a set of new product models through modular analysis of alternative attributes associated with the morphological approach; (2) to develop an effective assessment method that integrates the three dimensions of LCE analysis into a single decision support tool using the aggregation operator of weighted generalized means; and, (3) to search for improvement opportunities and refine preferred engineering options by means of the algorithm for inventive problem solving (ARIZ) methodology. The applicability of the ARIZ-based LCE model is demonstrated through a case study of an in-flight meal service.

Dynamic schedule execution in an agent based holonic manufacturing system

The present paper deals with the negotiation based task allocation to the resources for preparing dynamic scheduling in an agent based holonic control framework. The scheduling priority is developed by Multi Objective Optimization on the basis of Ratio Analysis (MOORA) technique under Fuzzy Multi Criteria Decision Making (FMCDM) environment considering several attributes. The well-known Contract Net Protocol (CNP) is followed for the purpose of task allocation by negotiation and cooperation, where message based communication is accomplished by eXtensible markup language (XML) using J2EE. Different Document Type Definitions (DTDs) are developed for intended applications. Necessary modifications in the scheduling arising out of changes in the volume-mix are made by a distributed cooperative problem-solving algorithm to meet the demand without violating the deadline. The algorithm is implemented using HTML code in front-end with Java Server Page (JSP) through Apache Tomcat 6.02 server. It is advocated that the cooperation based teamwork coupled with higher flexibility and agility is the key to success to remain unperturbed and provide reasonably good solution in the face of disturbances and stands superior to its hierarchical counterpart.