An error-propagation aware method to reduce the software mutation cost using genetic algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Seyed Mohammad Javad Hosseini ◽  
Bahman Arasteh ◽  
Ayaz Isazadeh ◽  
Mehran Mohsenzadeh ◽  
Mitra Mirzarezaee
Keyword(s):  
Genetic Algorithm ◽  
Error Propagation ◽  
Propagation Rate ◽  
Mutation Testing ◽  
Control Flow ◽  
Basic Block ◽  
Content Type ◽  
Mutation Score ◽  
Tool Set ◽  
Undecidable Problem

PurposeThe purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.Design/methodology/approachIn a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.FindingsIn order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.Originality/valueThe main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

Path optimization of CNC PCB drilling using hybrid Taguchi genetic algorithm

Kybernetes ◽  
2016 ◽  
Vol 45 (1) ◽  
pp. 107-125 ◽  
Author(s):  
Dony Hidayat Al-Janan ◽  
Tung-Kuan Liu
Keyword(s):  
Genetic Algorithm ◽  
Printed Circuit Boards ◽  
Numerical Control ◽  
Circuit Boards ◽  
Improved Genetic Algorithm ◽  
Solution Quality ◽  
Content Type ◽  
Neighbor Search ◽  
Feasible Solutions ◽  
The Traveling Salesman Problem

Purpose – In this study, the hybrid Taguchi genetic algorithm (HTGA) was used to optimize the computer numerical control-printed circuit boards drilling path. The optimization was performed by searching for the shortest route for the drilling path. The number of feasible solutions is exponentially related to the number of hole positions. The paper aims to discuss these issues. Design/methodology/approach – Therefore, a traveling cutting tool problem (TCP), which is similar to the traveling salesman problem, was used to evaluate the drilling path; this evaluation is considered an NP-hard problem. In this paper, an improved genetic algorithm embedded in the Taguchi method and a neighbor search method are proposed for improving the solution quality. The classical TCP problems proposed by Lim et al. (2014) were used for validating the performance of the proposed algorithm. Findings – Results showed that the proposed algorithm outperforms a previous study in robustness and convergence speed. Originality/value – The HTGA has not been used for optimizing the drilling path. This study shows that the HTGA can be applied to complex problems.

Development of a direct NGM(1,1) prediction model based on interval grey numbers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ye Li ◽  
Yuanping Ding ◽  
Yaqian Jing ◽  
Sandang Guo
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Prediction Model ◽  
Real Number ◽  
Content Type ◽  
Optimal Weight ◽  
Practical Applications ◽  
Number Sequences ◽  
Integral Development ◽  
Grey Number

PurposeThe purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.Design/methodology/approachA definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.FindingsThe interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.Practical implicationsUncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.Originality/valueThe main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

Interpreting the industry 4.0 future: technology, business, society and people

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Holger Schiele ◽  
Anna Bos-Nehles ◽  
Vincent Delke ◽  
Peter Stegmaier ◽  
Robbert-Jan Torn
Keyword(s):  
Business Strategy ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Business And Society ◽  
Holistic View ◽  
Content Type ◽  
Future Technology ◽  
Technological Advances ◽  
Tool Set ◽  
Strategy Design

Purpose Industrial revolutions have been induced by technological advances, but fundamentally changed business and society. To gain a comprehensive understanding of the fourth industrial revolution (I4.0) and derive guidelines for business strategy, it is, therefore, necessary to explore it as a multi-facet phenomenon. Most literature on I4.0, however, takes up a predominantly technical view. This paper aims to report on a project discussing a holistic view on I4.0 and its implications, covering technology, business, society and people. Design/methodology/approach Two consecutive group discussions in form of academic world cafés have been conducted. The first workshop gathered multi-disciplinary experts from academia, whose results were further validated in a subsequent workshop including industry representatives. A voting procedure was used to capture participants perspectives. Findings The paper develops a holistic I4.0 vision, focusing on five core technologies, their business potential, societal requests and people implications. Based on the model a checklist has been developed, which firms can use a tool to analyze their firm’s situation and draft their industry 4.0 business strategy. Originality/value Rather than focusing on technology alone – which by itself is unlikely to make up for a revolution – this research integrates the entire system. In this way, a tool-set for strategy design results.

Dual-position excitation technique in flow control over an airfoil at low speeds

2018 ◽  
Vol 30 (9) ◽  
pp. 4141-4154
Author(s):  
Abbas Ebrahimi ◽  
Majid Hajipour ◽  
Kamran Ghamkhar
Keyword(s):  
Flow Control ◽  
Shear Layer ◽  
Control Method ◽  
Lift Coefficient ◽  
Shear Layers ◽  
Control Flow ◽  
Plasma Actuators ◽  
Dbd Plasma ◽  
Surface Shear ◽  
Content Type

PurposeThe purpose of this paper is to control flow separation over a NACA 4415 airfoil by applying unsteady forces to the separated shear layers using dielectric barrier discharge (DBD) plasma actuators. This novel flow control method is studied under conditions which the airfoil angle of attack is 18°, and Reynolds number based on chord length is 5.5 × 105.Design/methodology/approachLarge eddy simulation of the turbulent flow is used to capture vortical structures through the airfoil wake. Power spectral density analysis of the baseline flow indicates dominant natural frequencies associated with “shear layer mode” and “wake mode.” The wake mode frequency is used simultaneously to excite separated shear layers at both the upper surface and the trailing edge of the airfoil (dual-position excitation), and it is also used singly to excite the upper surface shear layer (single-position excitation).FindingsBased on the results, actuations manipulate the shear layers instabilities and change the wake patterns considerably. It is revealed that in the single-position excitation case, the vortices shed from the upper surface shear layer are more coherent than the dual-position excitation case. The maximum value of lift coefficient and lift-to-drag ratio is achieved, respectively, by single-position excitation as well as dual-position excitation.Originality/valueThe paper contributes to the understanding and progress of DBD plasma actuators for flow control applications. Further, this research could be a beneficial solution for the promising design of advanced low speed flying vehicles.

scholarly journals Selection methods and diversity preservation in many-objective evolutionary algorithms

2018 ◽  
Vol 52 (4) ◽  
pp. 502-519 ◽  
Author(s):  
Luis Martí ◽  
Eduardo Segredo ◽  
Nayat Sánchez-Pi ◽  
Emma Hart
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Euclidean Distance ◽  
Selection Methods ◽  
Selection Scheme ◽  
Content Type ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Diversity Preservation ◽  
Promising Line

Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

Design optimisation for cold-formed steel residential roof truss using genetic algorithm

2018 ◽  
Vol 15 (5) ◽  
pp. 575-583
Author(s):  
Ka Yee Kok ◽  
Hieng Ho Lau ◽  
Thanh Duoc Phan ◽  
TIina Chui Huon Ting
Keyword(s):  
Genetic Algorithm ◽  
Industrial Application ◽  
Steel Structures ◽  
Single Type ◽  
Efficient Design ◽  
Design Optimisation ◽  
Content Type ◽  
Roof Truss ◽  
Cold Formed Steel ◽  
Multi Level

Purpose This paper aims to present the design optimisation using genetic algorithm (GA) to achieve the highest strength to weight (S/W) ratio, for cold-formed steel residential roof truss. Design/methodology/approach The GA developed in this research simultaneously optimises roof pitch, truss configurations, joint coordinates and applied loading of typical dual-pitched symmetrical residential roof truss. The residential roof truss was considered with incremental uniform distributed loading, in both gravitational and uplift directions. The structural analyses of trusses were executed in this GA using finite element toolbox. The ultimate strength and serviceability of trusses were checked through the design formulation implemented in GA, according to the Australian standard, AS/NZS 4600 Cold-formed Steel Structures. Findings An optimum double-Fink roof truss which possess highest S/W ratio using GA was determined, with optimum roof pitch of 15°. The optimised roof truss is suitable for industrial application with its higher S/W ratio and cost-effectiveness. The combined methodology of multi-level optimisation and simultaneous optimisation developed in this research could determine optimum roof truss with consistent S/W ratio, although with huge GA search space. Research limitations/implications The sizing of roof truss member is not optimised in this paper. Only single type of cold-formed steel section is used throughout the whole optimisation. The design of truss connection is not considered in this paper. The corresponding connection costs are not included in the proposed optimisation. Practical implications The optimum roof truss presented in this paper is suitable for industrial application with higher S/W ratio and lower cost, in either gravitational or uplift loading configurations. Originality/value This research demonstrates the approaches in combining multi-level optimisation and simultaneous optimisation to handle large number of variables and hence executed an efficient design optimisation. The GA designed in this research determines the optimum residential roof truss with highest S/W ratio, instead of lightest truss weight in previous studies.

scholarly journals An intelligence optimization method based on crowd intelligence for IoT devices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ke Wang ◽  
Zheming Yang ◽  
Bing Liang ◽  
Wen Ji
Keyword(s):  
Genetic Algorithm ◽  
Design Methodology ◽  
Rapid Development ◽  
Optimization Method ◽  
The Internet ◽  
Content Type ◽  
Communication Capacity ◽  
Iot Devices ◽  
Crowd Intelligence ◽  
The Internet Of Things

Purpose The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently. Design/methodology/approach In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices. Findings Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level. Originality/value This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

Investigation of magnet segmentation techniques for eddy current losses reduction in permanent magnets electrical machines

2015 ◽  
Vol 34 (1) ◽  
pp. 46-60 ◽  
Author(s):  
Belli Zoubida ◽  
Mohamed Rachid Mekideche
Keyword(s):  
Genetic Algorithm ◽  
Finite Elements ◽  
Eddy Current ◽  
Permanent Magnets ◽  
Skin Effect ◽  
Electrical Machines ◽  
Design Parameters ◽  
Finite Elements Analysis ◽  
Eddy Current Losses ◽  
Content Type

Purpose – Reducing eddy current losses in magnets of electrical machines can be obtained by means of several techniques. The magnet segmentation is the most popular one. It imposes the least restrictions on machine performances. This paper investigates the effectiveness of the magnet circumferential segmentation technique to reduce these undesirable losses. The full and partial magnet segmentation are both studied for a frequency range from few Hz to a dozen of kHz. To increase the efficiency of these techniques to reduce losses for any working frequency, an optimization strategy based on coupling of finite elements analysis and genetic algorithm is applied. The purpose of this paper is to define the parameters of the total and partial segmentation that can ensure the best reduction of eddy current losses. Design/methodology/approach – First, a model to analyze eddy current losses is presented. Second, the effectiveness of full and partial magnet circumferential segmentation to reduce eddy loss is studied for a range of frequencies from few Hz to a dozen of kHz. To achieve these purposes a 2-D finite element model is developed under MATLAB environment. In a third step of the work, an optimization process is applied to adjust the segmentation design parameters for best reduction of eddy current losses in case of surface mounted permanent magnets synchronous machine. Findings – In case of the skin effect operating, both full and partial magnet segmentations can lead to eddy current losses increases. Such deviations of magnet segmentation techniques can be avoided by an appropriate choice of their design parameters. Originality/value – Few works are dedicated to investigate partial magnet segmentation for eddy current losses reduction. This paper studied the effectiveness and behaviour of partial segmentation for different frequency ranges. To avoid eventual anomalies related to the skin effect an optimization process based on the association of the finite elements analysis to genetic algorithm method is adopted.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

Lubrication efficiency of crankpin bearing using square-cylindrical textures of partial textures optimized by genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shaoyong Xu ◽  
Vanliem Nguyen ◽  
Xiaoyan Guo ◽  
Huan Yuan
Keyword(s):  
Genetic Algorithm ◽  
Mixed Lubrication ◽  
Bearing Surface ◽  
Machining Time ◽  
Obvious Effect ◽  
Content Type ◽  
Density Distributions ◽  
Lubrication Regime ◽  
Mixed Lubrication Regime ◽  
Geometrical Dimensions

Purpose This paper aims to propose an optimal design of the partial textures in the mixed lubrication regime of the crankpin bearing (CB) to maximize the CB's lubrication efficiency. Design/methodology/approach Based on a hybrid model between the slider-crank-mechanism dynamic and CB lubrication, the square-cylindrical textures (SCT) of partial textures designed on the CB’s mixed lubrication regime are researched. The effect of the density distributions of partial textures on CB’s lubrication efficiency is then evaluated via two indices of increasing the oil film pressure (p) and decreasing the frictional force (Ff) of the CB. The SCT’s geometrical dimensions are then optimized by the genetic algorithm to further improve the CB’s lubrication efficiency. Findings The results show that the SCT of partial textures optimized by the genetic algorithm has an obvious effect on enhancing CB’s lubrication efficiency. Especially, with the CB using the optimal SCT of partial textures (4 × 6), the maximum p is significantly increased by 3.7% and 8.2%, concurrently, the maximum Ff is evidently reduced by 9.5% and 21.6% in comparison with the SCT of partial textures (4 × 6) without optimization and the SCT of full textures (12 × 6) designed throughout the CB’s bearing surface, respectively. Originality/value The application of the optimal SCT of partial textures on the bearing surface not only is simple for the design-manufacturing process and maximizes CB’s lubrication efficiency but also can reduce the machining time, save cost and ensure the durability of the bearing compared to use the full textures designed throughout the CB’s bearing surface.

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