This document discusses using metaheuristic search techniques to solve resource allocation and scheduling problems that are common in software development projects. It evaluates the performance of three algorithms - simulated annealing, tabu search, and genetic algorithms - on test problems representative of resource constrained project scheduling problems (RCPSP). The experimental results found that all three metaheuristics can solve such problems effectively, with genetic algorithms performing slightly better overall than the other two techniques.
This paper provides a short analytical critique of the white paper "An Examination of Software Engineering Work Practices" by Singer, Lethbridge, Vinson, and Anquetil. The critique argues that the methodology used in the study has biases and limitations. Specifically, it critiques the small sample size of studying one employee's activities over short sessions, and argues computer-based studies could provide more accurate data on software engineers' work practices. However, it acknowledges the value of the authors' contributions to research in this area. Ultimately, the critique concludes the arguments for dismissing other research methods and claims of success in developing a tool are debatable given weaknesses in the methodology.
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model.
The document discusses test case optimization using genetic and tabu search algorithms for structural testing. It proposes a hybrid algorithm that combines genetic algorithm and tabu search algorithm. Genetic algorithm is initially used to generate test cases, but it can get stuck in local optima. The hybrid approach uses tabu search to improve the mutation step of genetic algorithm. This helps guide the search away from previously visited solutions and avoid local optima, improving test case optimization. An experiment on a voter validation form showed the hybrid approach produced a more efficient test suite compared to genetic algorithm alone.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
INVESTIGATE,IDENTIFY AND ESTIMATE THE TECHNICAL DEBT: A SYSTEMATIC MAPPING STUDYijseajournal
This document summarizes a systematic mapping study that investigated technical debt (TD) in 43 empirical studies published between 2014-2017. The study classified the studies based on TD type, investigation method, detection/estimation techniques, and tools used. The most common TD types were code debt and design debt. Researchers most frequently investigated relationships between TD and other factors. The top indicators were smells and code comments, while effort was the most common estimator. SonarQube was the most used tool, though many researchers developed custom tools. The results provide insight into trends in TD research during that period.
INVESTIGATE,IDENTIFY AND ESTIMATE THE TECHNICAL DEBT: A SYSTEMATIC MAPPING STUDYijseajournal
Context: Technical Debt (TD) is a metaphor that refers to short-term solutions in software development that may affect the cost to the software development life cycle. Objective: To explore and understand TDrelated to the software industry as well as an overview on the current state of TD research. Forty-three TD empirical studies were collected for classification and analyzation. Goals: Classify TD types, find the indicators used to detect TD, find the estimators used to quantify the TD, evaluate how researchers investigate TD. Method: By performing a systematic mapping study to identify and analyze the TD empirical studies which published between 2014 and 2017. Results: We present the most common indicators and evaluators to identify and evaluate the TD, and we gathered thirteen types of TD. We showed some ways to investigate the TD, and used tools in the selected studies. Conclusion: The outcome of our systematic mapping study can help researchers to identify interestand future in TD.
Machine Reading Using Neural Machines (talk at Microsoft Research Faculty Sum...Isabelle Augenstein
The document discusses machine reading using neural machines. It presents goals of fact checking claims and understanding scientific publications. It outlines challenges in tasks like stance detection on tweets and summarizing scientific papers. These include interpreting statements based on the target or headline, handling unseen targets, and the small size of benchmark datasets which makes neural machine reading computationally costly.
This document summarizes a project report that evaluated the randomness of numbers generated by Random.org, a public true random number service. The project conducted a literature review on random number generators and statistical tests for randomness. It proposed a suite of statistical tests to apply daily to Random.org numbers and compared Random.org to other commonly used random number generators. While the tests found Random.org numbers to be completely random, the report noted some open issues for further consideration, such as improving the power and independence of statistical tests.
This paper provides a short analytical critique of the white paper "An Examination of Software Engineering Work Practices" by Singer, Lethbridge, Vinson, and Anquetil. The critique argues that the methodology used in the study has biases and limitations. Specifically, it critiques the small sample size of studying one employee's activities over short sessions, and argues computer-based studies could provide more accurate data on software engineers' work practices. However, it acknowledges the value of the authors' contributions to research in this area. Ultimately, the critique concludes the arguments for dismissing other research methods and claims of success in developing a tool are debatable given weaknesses in the methodology.
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model.
The document discusses test case optimization using genetic and tabu search algorithms for structural testing. It proposes a hybrid algorithm that combines genetic algorithm and tabu search algorithm. Genetic algorithm is initially used to generate test cases, but it can get stuck in local optima. The hybrid approach uses tabu search to improve the mutation step of genetic algorithm. This helps guide the search away from previously visited solutions and avoid local optima, improving test case optimization. An experiment on a voter validation form showed the hybrid approach produced a more efficient test suite compared to genetic algorithm alone.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
INVESTIGATE,IDENTIFY AND ESTIMATE THE TECHNICAL DEBT: A SYSTEMATIC MAPPING STUDYijseajournal
This document summarizes a systematic mapping study that investigated technical debt (TD) in 43 empirical studies published between 2014-2017. The study classified the studies based on TD type, investigation method, detection/estimation techniques, and tools used. The most common TD types were code debt and design debt. Researchers most frequently investigated relationships between TD and other factors. The top indicators were smells and code comments, while effort was the most common estimator. SonarQube was the most used tool, though many researchers developed custom tools. The results provide insight into trends in TD research during that period.
INVESTIGATE,IDENTIFY AND ESTIMATE THE TECHNICAL DEBT: A SYSTEMATIC MAPPING STUDYijseajournal
Context: Technical Debt (TD) is a metaphor that refers to short-term solutions in software development that may affect the cost to the software development life cycle. Objective: To explore and understand TDrelated to the software industry as well as an overview on the current state of TD research. Forty-three TD empirical studies were collected for classification and analyzation. Goals: Classify TD types, find the indicators used to detect TD, find the estimators used to quantify the TD, evaluate how researchers investigate TD. Method: By performing a systematic mapping study to identify and analyze the TD empirical studies which published between 2014 and 2017. Results: We present the most common indicators and evaluators to identify and evaluate the TD, and we gathered thirteen types of TD. We showed some ways to investigate the TD, and used tools in the selected studies. Conclusion: The outcome of our systematic mapping study can help researchers to identify interestand future in TD.
Machine Reading Using Neural Machines (talk at Microsoft Research Faculty Sum...Isabelle Augenstein
The document discusses machine reading using neural machines. It presents goals of fact checking claims and understanding scientific publications. It outlines challenges in tasks like stance detection on tweets and summarizing scientific papers. These include interpreting statements based on the target or headline, handling unseen targets, and the small size of benchmark datasets which makes neural machine reading computationally costly.
This document summarizes a project report that evaluated the randomness of numbers generated by Random.org, a public true random number service. The project conducted a literature review on random number generators and statistical tests for randomness. It proposed a suite of statistical tests to apply daily to Random.org numbers and compared Random.org to other commonly used random number generators. While the tests found Random.org numbers to be completely random, the report noted some open issues for further consideration, such as improving the power and independence of statistical tests.
The document describes an automated process for bug triage that uses text classification and data reduction techniques. It proposes using Naive Bayes classifiers to predict the appropriate developers to assign bugs to by applying stopword removal, stemming, keyword selection, and instance selection on bug reports. This reduces the data size and improves quality. It predicts developers based on their history and profiles while tracking bug status. The goal is to more efficiently handle software bugs compared to traditional manual triage processes.
IRJET- Missing Value Evaluation in SQL Queries: A SurveyIRJET Journal
This document summarizes research on evaluating missing values, or "why-not" questions, in SQL queries. It surveys techniques used to answer why-not questions for both numeric and non-numeric data. The document compares strategies like query refinement, index-based algorithms, and ranking functions. It also outlines future work on applying these techniques to social network and graph queries. The goal is to make database systems more transparent and interactive by supporting exploratory analysis of missing answers.
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
Large Scale Studies: Malware Needles in a HaystackMarcus Botacin
Large-scale, parallel processing of malware samples
Processamento de logs de malware em larga escala através de paralelização.
Apresentação referente a co-orientação do projeto de iniciação científica do aluno Giovanni Bertão.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
130321 zephyrin soh - on the effect of exploration strategies on maintenanc...Ptidej Team
RQ2
RQ3
RQ4
Conclusion and
Future Work
Conclusion
Threats to Validity and
Future Work
9 / 30
This document presents an empirical study that investigates developers' program exploration strategies. The goal is to understand how developers navigate through a program's entities in order to help them more efficiently. The study analyzes developers' interaction histories to identify common exploration strategies and examines relationships between strategies and other factors like task type and expertise level. The results could help evaluate developer performance, improve comprehension models, and guide less experienced developers.
This document discusses a process calculus for modeling spatially-explicit ecological models. It begins with an introduction to spatially-explicit ecological models and motivations. Related work discusses existing approaches for modeling chemical reactions and ecological systems, including stochastic simulation, P systems, and process calculi. The document then describes the existing PALPS process calculus for population systems and its operational semantics and modeling capabilities. Finally, it outlines several research questions around extending the PALPS calculus with continuous time and dynamic parameters, and comparing it to other modeling approaches through translation and identifying other advantages beyond model checking.
Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible -- e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges.
Sca a sine cosine algorithm for solving optimization problemslaxmanLaxman03209
The document proposes a new population-based optimization algorithm called the Sine Cosine Algorithm (SCA) for solving optimization problems. SCA creates multiple random initial solutions and uses sine and cosine functions to fluctuate the solutions outward or toward the best solution, emphasizing exploration and exploitation. The performance of SCA is evaluated on test functions, qualitative metrics, and by optimizing the cross-section of an aircraft wing, showing it can effectively explore, avoid local optima, converge to the global optimum, and solve real problems with constraints.
It Does What You Say, Not What You Mean: Lessons From A Decade of Program RepairClaire Le Goues
In this talk we present lessons learned, good ideas, and thoughts on the future, with an eye toward informing junior researchers about the realities and opportunities of a long-running project. We highlight some notions from the original paper that stood the test of time, some that were not as prescient, and some that became more relevant as industrial practice advanced. We place the work in context, highlighting perceptions from software engineering and evolutionary computing, then and now, of how program repair could possibly work. We discuss the importance of measurable benchmarks and reproducible research in bringing scientists together and advancing the area. We give our thoughts on the role of quality requirements and properties in program repair. From testing to metrics to scalability to human factors to technology transfer, software repair touches many aspects of software engineering, and we hope a behind-the-scenes exploration of some of our struggles and successes may benefit researchers pursuing new projects.
This paper discusses the several research methodologies that can
be used in Computer Science (CS) and Information Systems
(IS). The research methods vary according to the science
domain and project field. However a little of research
methodologies can be reasonable for Computer Science and
Information System.
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...Johann Petrak
Slides for the talk about the paper:
Ziqi Zhang, Johann Petrak and Diana Maynard, 2018: Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms. Semantics-2018, Vienna, Austria
An Iterative Model as a Tool in Optimal Allocation of Resources in University...Dr. Amarjeet Singh
In this paper, a study was carried out to aid in
adequate allocation of resources in the College of Natural
Sciences, TYZ University (not real name because of ethical
issue). Questionnaires were administered to the highranking officials of one the Colleges, College of Pure and
Applied Sciences, to examine how resources were allocated
for three consecutive sessions(the sessions were 2009/2010,
2010/2011 and 2011/2012),then used the data gathered and
analysed to generate contributory inputs for the three basic
outputs (variables)formed for the purpose of the study.
These variables are: 1
x
represents the quality of graduates
produced;
2
x
stands for research papers, Seminars,
Journals articles etc. published by faculties and
3
x
denotes service delivery within the three sessions under study.
Simplex Method of Linear Programming was used to solve
the model formulated.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
Improvement of genetic algorithm using artificial bee colonyjournalBEEI
This document proposes using an artificial bee colony algorithm to improve a genetic algorithm. It does this by generating the initial population for the genetic algorithm rather than using random generation. The proposed method is tested on random number generation and the travelling salesman problem. For random number generation, five statistical tests are used to evaluate fitness, with the goal of generating random numbers that pass all tests. For the travelling salesman problem, fitness is based on minimizing the total distance travelled. The results show the proposed method performs better than the traditional genetic algorithm in terms of mean iterations, execution time, error rate, and finding the shortest route.
SemEval 2017 Task 10: ScienceIE – Extracting Keyphrases and Relations from Sc...Isabelle Augenstein
Shared task summary for SemEval 2017 Task 10: ScienceIE – Extracting Keyphrases and Relations from Scientific Publications
Paper: https://arxiv.org/abs/1704.02853
Abstract:
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.
Multidisciplinary analysis and optimization under uncertaintyChen Liang
The document summarizes Chen Liang's doctoral dissertation research on multidisciplinary analysis and optimization under uncertainty. The research objectives are to develop efficient uncertainty quantification techniques for feedback-coupled multidisciplinary analysis and multidisciplinary design optimization that can account for both aleatory and epistemic sources of uncertainty. Specific areas of focus include representation of epistemic uncertainty, propagation of uncertainty through coupled analysis, and inclusion of uncertainty in high-dimensional multidisciplinary design optimization problems.
Comparisons of linear goal programming algorithmsAlexander Decker
This document compares different algorithms for solving linear goal programming problems:
1) Lee's modified simplex algorithm from 1972 and Ignizio's sequential algorithm from 1976 are two commonly used algorithms but require many columns and objective function rows, adding to computational time.
2) Orumie and Ebong developed a new algorithm in 2011 utilizing modified simplex procedures that has better computational times than existing algorithms for all problems tested.
3) The document reviews several other goal programming algorithms and finds that Orumie and Ebong's new method provides the best reduction in computational time for solving the problems.
El documento describe las redes de computadoras y sus características. Define una red como un conjunto de equipos conectados para compartir información y recursos mediante dispositivos físicos. Explica que existen diferentes tipos de redes según su tamaño, medio físico y topología de conexión.
The document describes an automated process for bug triage that uses text classification and data reduction techniques. It proposes using Naive Bayes classifiers to predict the appropriate developers to assign bugs to by applying stopword removal, stemming, keyword selection, and instance selection on bug reports. This reduces the data size and improves quality. It predicts developers based on their history and profiles while tracking bug status. The goal is to more efficiently handle software bugs compared to traditional manual triage processes.
IRJET- Missing Value Evaluation in SQL Queries: A SurveyIRJET Journal
This document summarizes research on evaluating missing values, or "why-not" questions, in SQL queries. It surveys techniques used to answer why-not questions for both numeric and non-numeric data. The document compares strategies like query refinement, index-based algorithms, and ranking functions. It also outlines future work on applying these techniques to social network and graph queries. The goal is to make database systems more transparent and interactive by supporting exploratory analysis of missing answers.
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
Large Scale Studies: Malware Needles in a HaystackMarcus Botacin
Large-scale, parallel processing of malware samples
Processamento de logs de malware em larga escala através de paralelização.
Apresentação referente a co-orientação do projeto de iniciação científica do aluno Giovanni Bertão.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
130321 zephyrin soh - on the effect of exploration strategies on maintenanc...Ptidej Team
RQ2
RQ3
RQ4
Conclusion and
Future Work
Conclusion
Threats to Validity and
Future Work
9 / 30
This document presents an empirical study that investigates developers' program exploration strategies. The goal is to understand how developers navigate through a program's entities in order to help them more efficiently. The study analyzes developers' interaction histories to identify common exploration strategies and examines relationships between strategies and other factors like task type and expertise level. The results could help evaluate developer performance, improve comprehension models, and guide less experienced developers.
This document discusses a process calculus for modeling spatially-explicit ecological models. It begins with an introduction to spatially-explicit ecological models and motivations. Related work discusses existing approaches for modeling chemical reactions and ecological systems, including stochastic simulation, P systems, and process calculi. The document then describes the existing PALPS process calculus for population systems and its operational semantics and modeling capabilities. Finally, it outlines several research questions around extending the PALPS calculus with continuous time and dynamic parameters, and comparing it to other modeling approaches through translation and identifying other advantages beyond model checking.
Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible -- e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges.
Sca a sine cosine algorithm for solving optimization problemslaxmanLaxman03209
The document proposes a new population-based optimization algorithm called the Sine Cosine Algorithm (SCA) for solving optimization problems. SCA creates multiple random initial solutions and uses sine and cosine functions to fluctuate the solutions outward or toward the best solution, emphasizing exploration and exploitation. The performance of SCA is evaluated on test functions, qualitative metrics, and by optimizing the cross-section of an aircraft wing, showing it can effectively explore, avoid local optima, converge to the global optimum, and solve real problems with constraints.
It Does What You Say, Not What You Mean: Lessons From A Decade of Program RepairClaire Le Goues
In this talk we present lessons learned, good ideas, and thoughts on the future, with an eye toward informing junior researchers about the realities and opportunities of a long-running project. We highlight some notions from the original paper that stood the test of time, some that were not as prescient, and some that became more relevant as industrial practice advanced. We place the work in context, highlighting perceptions from software engineering and evolutionary computing, then and now, of how program repair could possibly work. We discuss the importance of measurable benchmarks and reproducible research in bringing scientists together and advancing the area. We give our thoughts on the role of quality requirements and properties in program repair. From testing to metrics to scalability to human factors to technology transfer, software repair touches many aspects of software engineering, and we hope a behind-the-scenes exploration of some of our struggles and successes may benefit researchers pursuing new projects.
This paper discusses the several research methodologies that can
be used in Computer Science (CS) and Information Systems
(IS). The research methods vary according to the science
domain and project field. However a little of research
methodologies can be reasonable for Computer Science and
Information System.
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...Johann Petrak
Slides for the talk about the paper:
Ziqi Zhang, Johann Petrak and Diana Maynard, 2018: Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms. Semantics-2018, Vienna, Austria
An Iterative Model as a Tool in Optimal Allocation of Resources in University...Dr. Amarjeet Singh
In this paper, a study was carried out to aid in
adequate allocation of resources in the College of Natural
Sciences, TYZ University (not real name because of ethical
issue). Questionnaires were administered to the highranking officials of one the Colleges, College of Pure and
Applied Sciences, to examine how resources were allocated
for three consecutive sessions(the sessions were 2009/2010,
2010/2011 and 2011/2012),then used the data gathered and
analysed to generate contributory inputs for the three basic
outputs (variables)formed for the purpose of the study.
These variables are: 1
x
represents the quality of graduates
produced;
2
x
stands for research papers, Seminars,
Journals articles etc. published by faculties and
3
x
denotes service delivery within the three sessions under study.
Simplex Method of Linear Programming was used to solve
the model formulated.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
Improvement of genetic algorithm using artificial bee colonyjournalBEEI
This document proposes using an artificial bee colony algorithm to improve a genetic algorithm. It does this by generating the initial population for the genetic algorithm rather than using random generation. The proposed method is tested on random number generation and the travelling salesman problem. For random number generation, five statistical tests are used to evaluate fitness, with the goal of generating random numbers that pass all tests. For the travelling salesman problem, fitness is based on minimizing the total distance travelled. The results show the proposed method performs better than the traditional genetic algorithm in terms of mean iterations, execution time, error rate, and finding the shortest route.
SemEval 2017 Task 10: ScienceIE – Extracting Keyphrases and Relations from Sc...Isabelle Augenstein
Shared task summary for SemEval 2017 Task 10: ScienceIE – Extracting Keyphrases and Relations from Scientific Publications
Paper: https://arxiv.org/abs/1704.02853
Abstract:
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.
Multidisciplinary analysis and optimization under uncertaintyChen Liang
The document summarizes Chen Liang's doctoral dissertation research on multidisciplinary analysis and optimization under uncertainty. The research objectives are to develop efficient uncertainty quantification techniques for feedback-coupled multidisciplinary analysis and multidisciplinary design optimization that can account for both aleatory and epistemic sources of uncertainty. Specific areas of focus include representation of epistemic uncertainty, propagation of uncertainty through coupled analysis, and inclusion of uncertainty in high-dimensional multidisciplinary design optimization problems.
Comparisons of linear goal programming algorithmsAlexander Decker
This document compares different algorithms for solving linear goal programming problems:
1) Lee's modified simplex algorithm from 1972 and Ignizio's sequential algorithm from 1976 are two commonly used algorithms but require many columns and objective function rows, adding to computational time.
2) Orumie and Ebong developed a new algorithm in 2011 utilizing modified simplex procedures that has better computational times than existing algorithms for all problems tested.
3) The document reviews several other goal programming algorithms and finds that Orumie and Ebong's new method provides the best reduction in computational time for solving the problems.
El documento describe las redes de computadoras y sus características. Define una red como un conjunto de equipos conectados para compartir información y recursos mediante dispositivos físicos. Explica que existen diferentes tipos de redes según su tamaño, medio físico y topología de conexión.
Este documento describe los conceptos básicos de las redes de computadoras. Explica que una red es un conjunto de equipos conectados para compartir información y recursos. Luego describe los diferentes tipos de redes según su alcance, conexión, topología y otros factores. Finalmente, define los componentes clave de una red como servidores, estaciones de trabajo, tarjetas de red y cableado, e indica que el objetivo principal de una red es permitir el intercambio de información entre dispositivos.
An application of genetic algorithms to time cost-quality trade-off in constr...Alexander Decker
This document summarizes a research paper that develops an optimization model using genetic algorithms to solve the time-cost-quality trade-off problem in construction projects. The model aims to find the minimum cost for a construction project to meet certain quality levels within a given time limit. It does this by considering different activity execution modes and using genetic algorithms to efficiently explore the large solution space. The document provides background on optimization problems and techniques, an overview of the time-cost-quality trade-off problem and prior related research, and describes the objectives and approach of the developed genetic algorithms model.
A Comparative Study between Agile Methods of Software DevelopmentFelipe Alves
The document presents an extension of a comparative study between agile software development methods. It analyzes methods such as XP, Scrum, Crystal, FDD, DSDM, ASD, Kanban, Agile Modeling, OpenUP, and AgileUP based on key points, main features, and limitations. The study aims to help organizations choosing the most suitable agile method for their software projects and to spread knowledge about these methods.
Computational optimization, modelling and simulation: Recent advances and ove...Xin-She Yang
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The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
The operation research book that involves all units including the lpp problems, integer programming problem, queuing theory, simulation Monte Carlo and more is covered in this digital material.
This document reviews applications of evolutionary multiobjective optimization (EMO) techniques in production research. It summarizes EMO applications in several areas of production research, including scheduling, production planning and control, cellular manufacturing, flexible manufacturing systems, and assembly-line optimization. The review finds that EMO techniques have been successfully applied to optimization problems in these areas and provide a number of non-dominated solutions. However, future research opportunities remain, such as improved integration of EMO with other metaheuristics and consideration of additional objectives.
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
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This document provides a review of optimization algorithms that have been used to solve job shop scheduling problems (JSSP). It first discusses how JSSPs are NP-hard combinatorial optimization problems that are difficult to solve exactly. It then reviews both traditional and non-traditional algorithms that have been applied to JSSPs, including mathematical programming approaches, heuristic construction methods, evolutionary algorithms like genetic algorithms, and local search methods like simulated annealing and tabu search. The document also discusses metaheuristic algorithms and provides a classification of different metaheuristics. Overall, the document aims to assess the various techniques that have been used to approach solving JSSPs.
An Essay Concerning Human Understanding Of Genetic ProgrammingJennifer Roman
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A GROUNDED THEORY OF THE REQUIREMENTS ENGINEERING PROCESSijseajournal
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This document summarizes Muhammad Adil Raja's research interests in machine learning theory and applications. Specifically, his interests are: (1) developing a thorough understanding of theoretical machine learning concepts, algorithms, and subdomains; and (2) effectively applying machine learning to solve real-world problems. His past research focused on speech quality estimation using genetic programming. Currently, he is interested in exploring hyper-heuristics, which operate on heuristics rather than solutions, using genetic programming to generate and test heuristics for problems.
Application of Linear Programming to Profit Maximization (A Case Study of.pdfBrittany Allen
This document summarizes a research article that applies linear programming to maximize profit for Johnsons Nigeria Limited's bakery division. The article establishes a linear programming model to determine the optimal production mix of four bread sizes to maximize total profit given production capacity constraints. An initial simplex tableau is constructed and the optimal solution is obtained through iterative pivoting. The optimal solution shows producing three units of large bread yields the maximum profit of 150 naira. Sensitivity analysis confirms the optimal solution remains valid if the large bread's profit coefficient remains between 50-30 naira.
A Survey on Design Pattern Detection ApproachesCSCJournals
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
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Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
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6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
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8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
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11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
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Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
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-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
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This video focuses on integration of Salesforce with Bonterra Impact Management.
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problems found in the management of software development projects. In particular there is a
focus on resource and scheduling problems that have already been investigated in previously
published work in order to allow a comparison of the results to be made.
2. BACKGROUND & RELATED WORK
2.1. Search Based Software Engineering
The history of SBSE predates the term itself, with early research in representing software
engineering challenges as a search problem dating back to 1976 [2]. Early approaches represented
problems to be solved using classical techniques such as linear programming. However, Clark et
al. [3] and Harman [4]suggest that linear programming models are not the best option for solving
optimisation problems and this is because there are instances where the problem has certain
objectives which cannot be represented with linear algorithms, furthermore, these problems also
have multiple characteristics and fitness functions. Clarke et al. (2003) and Harman (2007) have
identified three areas where problems could persist when implementing metaheuristics search
techniques, but they have also provided potential solution to overcome the problems. One area in
which there has been only limited interest is that of software project planning.
2.1.1. Software Project Planning& Resourcing
The software engineering discipline has been in existence for a long time and since its
introduction there have been substantial introduction of project management techniques to
manage development projects. Over the years, there has been extensive publication in the area of
project management and scheduling. Herroelen [5] has further suggested that there is an
abundance of literature in this area, but for several reasons the theories have not been
implemented into practice. Project management in the discipline of software engineering has
always been problematic for many practitioners and there could be several reasons for it.
Herroelen [5] argued that these problems are mainly caused because of the following reasons:
•
•
•
•
•
Poor project management skills
Poor leadership skills
Size of the projects
Lack of resources
Inappropriate cost estimation and allocation methods
Furthermore, Herroelen[5] has also mentioned that these problems have been identified by
literature in the past. To overcome the above mentioned problems, Herroelen has proposed a
hierarchical project management model. In interest of solving the above mentioned problems,
more effectively, it has been suggested to use heuristics approaches and there is a growing body
of literature whereby researchers and practitioners have used algorithms to solve project
management and scheduling problems.
Resource Constrained Project Scheduling Problems (RCPSP) is a subsection of the issue
identified with in the software project planning and literature. This paper makes use of searchbased software engineering to resolve test examples that fall with in this class of problem.
Kolisch & Hartmann [6] have argued that the problem with software project planning is a high
level problem and when the problems are analysed further, it turns out that in most cases the
problems were caused because the resources were scarce. Furthermore, Pinto, Ainbinder &
Rabinowitz [7] have argued that there are three main resources which are usually scarce in a
software project and they are as follows:
3. Computer Science & Information Technology (CS & IT)
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•
•
355
Lack of human resource
Lack of funding
Lack of available time
The above mentioned categories are similar to Herroelen [5] whereby he was trying to explain
reasons for failure or escalation of a project, but having said irresolvable constraintscan also cause
the project to fail or escalate. Kolisch and Hartmann [6] have suggested that literature in the past
indicates that if a software project falls within the definition of RCPSP, then it is very likely that
project will either fail or be escalated. This is the main justification stated by Kolisch and
Hartmann [6] in support of their research to solve classes of RCPSP. Many researchers have
argued that literature in the past suggest that researchers and practitioners have used several
different methodologies to solve RCPSP, but unfortunately, none of the methodologies have been
successful implemented in the “real-world”.
Kolisch& Hartmann [6] have clearly extended the thoughts of Clarke et al. [3] by conducting
experiments to resolve this problem (i.e. implementing search techniques to solve RCPSP).
Having said that, Kolisch& Hartmann [6] have conducted experiments based on their assumptions
and their own past research in 2001 which could make this research biased, but on the other hand
Gueorguiev, Harman, &Antoniol [8] have conducted experiments using data from the “realworld” and this could potentially return results which are not biased.
Kolisch& Hartmann [6] and Gueorguiev, Harman, &Antoniol[8] all have mainly focused on
solving RCPSP using search-based software engineering approaches. The authors have clearly
followed the guidelines provided by Harman and Jones [1] and Clarke et al. [3] whereby they
reformulated the RCPSP as search problem. In the next stage authors have selected a
representation of the problem and after that, authors have identified their fitness functions to
evaluate candidate solutions. Having said that, each research had different criteria for fitness
function and this mainly because the nature of the experiments was different.
3. METHEURISTIC SEARCH ALGORITHMS
Metaheuristic search algorithms have been an area of growing interest for several decades as the
recent growth in computing power has resulted in the potential of these approaches being realised.
A wide range of algorithms have been developed, each of which has its own merits. This research
is not intended to be an exhaustive exploration of the performance of every algorithm and is
restricted to three standard algorithms, namely Simulated Annealing, Tabu Search, and Genetic
Algorithms.
2.1. Simulated Annealing
Simulated annealing is a metaheuristic search technique which can be used to solve optimisation
problems. The technique has the ability to find solutions in large and small solution spaces.
Unlike many other metaheuristic search techniques, this technique is a direct search method
involving a single search trajectory [9]. The name and inspiration for this search technique was
derived from the process of annealing metals. This annealing process involved heating and
gradually cooling the solid material so that the defects are reduced. After the completion of this
process, it can be concluded that the solid material has reached a global minimum state.
The simulated annealing algorithm is therefore very straight forward. When the algorithm is
initiated, an initial solution to the problem is randomly generated. After initial value is selected, it
is evaluated in accordance to the problem cost function and then changed slightly to generate a
new candidate solution from the neighbourhood of the initial solution. After selecting a new
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candidate solution, the value of the cost function is obtained and if the value is better than
previous candidate solutions then it is retained. However, if the value is worse than any other
candidate solution, then there is small probability that the search will move to the next candidate
solution and continue. The calculation of the probability is calculated using an analogy to the
Maxwell-Boltzmann probability function.
When there is a change in the value of the cost function, it determines the change in energy. The
units of temperature control parameters and cost function is the same. Additionally, the
temperature control parameter also enables the probability of selection. During the initial stages
of the execution process of the algorithm, the temperature is kept steady and this allows the
system to gain momentum in searching. As the temperature drops, the probability of selecting a
bad solution reduces. Hence towards the end, this algorithm tends to move towards an optimum
solution. Previous work [10] has shown that Simulated Annealing and Tabu Search both have the
capacity to solve complex problems, but with different solution trajectories.
2.2. Tabu Search
Tabu search has similar search method characteristics to simulated annealing and is generally
implemented as a single search trajectory direct search method. The concept was originally
coined by Glover [11, 12] and since then the application of this search technique has increased
considerably. Tabu search has been successfully implemented to solve discrete combinatorial
optimisation problems such as graph colouring and Travelling Salesman Problems, and has also
been applied to a range of practical problems. In terms of operations, tabu search is initiated at a
random starting point within a solution. After that, it identifies sequences of moves and whilst
that process is executed, a tabu list is generated. Evaluation of cost function can determine
whether the member belongs to the tabu list or not. Some members of the tabu list can belong to
an aspiring set. The criteria for aspiring move are dependent on the size and the type of the
problems; hence this could differ for each implementation. Additionally, tabu search also uses
tabu restrictions and a number of flexible memories with different time cycles. The flexible
memories allow search information to be exploited more thoroughly than rigid memory or
memoryless systems, and can be used to either intensify or diversify the search to force the
method to find optimum solutions. Previous work has shown that Tabu Search has the potential to
find solutions to complex problems much more efficiently than Genetic Algorithms [13].
2.3. Genetic Algorithms
Unlike simulated annealing and tabu search, genetic algorithm is not a local search method. This
search technique uses a population of solutions that are manipulated independently of the
evaluation of the cost function. This algorithm was built on the principles of Darwinian Evolution
[14]. Since its introduction, this search technique has been used in variety of disciplines and there
is substantial research to identify its practical implementations.
Goldberg [14] further adds that genetic algorithmsare a non-derivative based optimisation
technique and the outcome of this algorithm is based upon the principle of the survival of the
fittest. When the algorithm is initiated, a candidate solution set is created on random and this is
called a population. Using the existing population, new generation is created using genetic
operators like crossover, mutation, and reproduction. Ideally as the algorithm progresses, the
solutions are improved and optimum solutions can be achieved over time.
Genetic Algorithms are a broad and effective search method which has been applied to a wide
range of practical problems. The term Genetic Algorithm is particularly broad and covers many
5. Computer Science & Information Technology (CS & IT)
357
variations in implementation ranging from the simple GA presented by Golberg[14] through to
complex multi-objective algorithms such as NSGA-II [15].
4. TEST PROBLEMS
Each of the algorithms described in Section 3 have been implemented and tested on a number of
different test problems. Prior to investigating resourcing and scheduling problems, the
performance and scalability of the implementations were tested on numerical test functions and
other discrete optimisation problems, such as the n-Queens problem. This is not reported in this
paper but allowed for each algorithm to be suitably tuned to allow a fair comparison to be made.
4.1. Resourcing Problem
To schedule a project effectively, project planners must select appropriate costing and resourcing
options. This selection will determine the duration of the project. In most cases, projects have
multiple costing and resourcing options which lead to multiple due dates. The main objective in
the evaluation is to schedule resource unconstrained and constrained project using metaheuristics
search techniques.
Traditionally, project schedules can be generated using a critical path method and that project
planners can also include resources and activities assigned to those resources. Unfortunately, such
schedules have a down side whereby it is difficult for project planners to identify when the
resources were freed from the previous activity. Hence this evaluation will overcome the
limitation identified by using critical path method. Before the evaluation process starts, consider a
small project presented in Table 1 by each activity with its early start, early finish, late start, late
finish and total float.
Table 1.Project Scheduling Data [16]
This data was used by Christodoulou [16] to schedule the project using ant colony optimisation
algorithm. The critical path calculations on the above mentioned case study topology and the
resulting early start, early finish, late start, late finish and total float can be solved by applying
traditional critical path planning methods. Based on the critical path method calculation and
activities 4, 10 and 17 have been identified as critical and the total duration of the project is 126
time units.
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Computer Science & Information Technology (CS & IT)
Christodoulou [16] has also solved the above mentioned case study using critical path method
resource unconstrained and resource constrained environments. Results are presented in section 5
environment
that can be compared to the work of Christodoulou [16].Figure 1 illustrates the critical path for
this small project.
Figure 1.Critical path
4.2. Scheduling Problem
This paper also evaluates the performance of three meta-heuristic algorithms on a multi-objective
meta heuristic
multi
time-cost trade-off project scheduling problem which is discrete in nature. The problem data is
off
presented in Table 2. This data has also used by Elbeltagi, Hegazy& Grierson [17] and Feng, Liu
& Burns [18] to solve discrete optimisation problem by implementing a number of different
algorithms.
7. Computer Science & Information Technology (CS & IT)
359
Table 2.Project Scheduling Data [17]
The data presented above relates to a project which constitutes 18 activities and has been
presented with 5 options of different cost and duration. In each case, the first option is the most
expensive option but it will take the least number of days to complete the project and the fifth
option is the cheapest option and it will take the longest to complete. For each task the project
managers would have to choose from five options and this could traditionally be done using
heuristics approaches, but to get most optimised solution, one of the five options will be selected
for each task using genetic algorithm, simulated annealing and tabu search. As mentioned earlier,
this data is related to time-cost trade-off problem and as such there is likely to be a pareto-optimal
set of solutions to the problem. Thepareto-optimal set of solutions is a unique line through the
total set of solutions that represents what are considered to be non-dominated solutions. Each
solution along the pareto-optimal front is equally valid in terms of how it trades off cost and time.
Before the evaluations are carried out, a critical path must be established for the 18 tasks
mentioned above and it illustrated in Figure 2.
5
1
7
11
8
12
6
17
9
2
Start
3
4
10
15
13
14
16
Figure 2.Task dependency network
18
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This task dependency network does not display a critical path as the selection of different options
from Table 2 may result in different critical paths being generated. The objective of this problem
is to minimise the total cost of the project and to do this a fitness function has been used against
each algorithm. This fitness function is defined by Equation 1.
݂ሺݔሻ = ܰܫܯቌሺܶ × ܫሻ + ܥ ቍ
ୀଵ
Equation 1: Cost Estimation Fitness Function
The variables mentioned in the above mentioned fitness function represents the following:
n = number of activities
Cij= direct cost of activity i using its method of construction j
T = total project duration
I = daily indirect cost
The three different metaheuristic search algorithms are used to minimise the total cost of the
project using the fitness function mentioned above. The underlying application and parameters for
each algorithm is similar to the previous evaluations. The results generated from these evaluations
will be compared against the results from evaluations carried out by Elbeltagi, Hegazy& Grierson
[17] and Feng, Liu & Burns [18].
5. EXPERIMENTAL RESULTS
5.1. Resourcing Problem
Resource unconstrained scheduling is fairly straight forward and in most cases can be solved
using critical path methods. For the purpose of this evaluation, the genetic algorithm, simulated
annealing and tabu search algorithms will be used. Because the case study is relatively simple, all
search techniques were able to find an optimum solution in a reasonable timescale. In this case
the optimum solution is 126 time units for the project duration. When this solution is compared
against the solution presented using critical path method it is the same.
Although in this case the solution is the same as critical path method, it may always not be the
same. If the size of the project would be extensively large then finding an optimum project
duration would take longer and may not be correct because of human intervention. In this case
study, the critical path method calculation required ten conditional statements and 17 additions /
subtractions for each forward or backward pass in the network. In contrast to that the
metaheuristic search algorithms are more efficient in finding the optimum solution. The
advantage of this might not be so obvious in this evaluation mainly because of the size of the
data, but it is likely that for larger dataset these algorithms would generate results significantly
faster and more efficiently.
Table 3. Unconstrained Resourcing Problem Results
Algorithm
Duration Critical Path Activities Iterations
126
4, 10, 17
<= 50
Ant Colony Optimisation [16]
126
4, 10, 17
<= 39
Genetic Algorithm
126
4, 10, 17
<= 49
Simulated Annealing
126
4, 10, 17
<= 55
Tabu Search
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The results presented above for each algorithm are the same and that is mainly because there are
no constraints on the project. However, some search techniques have found the optimum solution
sooner than other search techniques. In this case, genetic algorithm was the quickest to find the
best solution. In the evaluations carried out by Christodoulou [16], he has also achieved the same
results as genetic algorithm, simulated annealing and tabu search. Although the size of the case is
study is fairly small the overall process for calculating the total duration and identifying critical
activities was very straight forward. The main idea behind this evaluation is to schedule the
project as soon as possible, hence any constraints were not considered. However, if we were to
assign resources constraint to each task and still wanted to same project due date, there would be
some over allocated resources.
Scheduling a resource-unconstrained project is reasonably straightforward, but as soon as there is
a constraint on resources for the project, the scheduling becomes very complicated and critical
path method may not be sufficient to achieve an optimised project schedule. The lack of resources
needed to start and complete an activity make certain critical paths unfeasible solutions.As a
result, some of the activities in a project can be put on hold which in turn can impact the entire
project schedule. In the standard critical path method the importance of activities are determined
by its total float value. The importance of activity increases as the value of total float drops.
Hence, when scheduling a project activities with fewer totals float value get preference in
allocating resources.
In the unconstrained problem, it is assumed that each activity in the problem presented in Table 1
utilises one unit of resources for each day and based on that a resource histogram is can be
generated. However for this evaluation it is assumed that the availability of resource is
constrained to 7 units. As a result the need for resources has exceeded the available resource
threshold. When the constraints are implemented the results shown in Table 4 are achieved.
Table 4.Constrained Resourcing Problem Results
Algorithm
Duration Critical Path Activities Iterations
142
3, 13, 15
<= 50
Ant Colony Optimisation [16]
139
4, 7, 17
<= 58
Genetic Algorithm
147
5, 9, 17
<= 55
Simulated Annealing
143
2, 9, 17
<= 62
Tabu Search
This table represents time taken in the duration column, and also highlights the critical activity.
The first results are derived from the experiments of Christodoulou [16]. In his experiments, ant
colony optimisation finds a solution that takes 142 time units to complete a project and in
comparison that genetic algorithm implemented in this research will take 139 time units and the
critical activities are 4, 7 and 17. Whilst the genetic algorithm has found the solution by
projecting to complete the project in 139 time units, it took more iterations than ant colony
optimisation and simulated annealing. Ant colony optimisation has outperformed simulated
annealing and tabu search in terms of both duration of the outcome and the number of iterations
required to find the solution.
5.2. Scheduling Problem
The summary of results generated from this evaluation is presented in Table 5. This table
represents the minimum and average of project cost and duration over multiple runs of the
algorithms. In addition to this, it also presents the percentage of success against the other
algorithms. The percentage of success is calculated based on numbers of days and total cost of the
project. Hence, the lower the total cost of project and duration, higher the success rate of the
algorithm.
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Table 5.Scheduling Problem Results
Algorithm
Duration
Genetic Algorithm
Simulated Annealing
Tabu Search
104
110
108
Minimum
Cost
Iterations
139,320
145,820
156,720
64
77
71
Duration
111
118
113
Average
Cost
Iterations
152,010
156,310
156,910
68
80
75
% Success
50
30
20
The genetic algorithm was again the best performing algorithm by finding an option for project
managers to complete the project in 104 days with total cost of $139,320. The best combination
found by simulated annealing was to complete the project in 110 days with total cost of $145,820
and the best combination found by tabu search was to complete the project in 108 days with total
he
cost of $156,720. Although the combination found by tabu search enables the project to complete
156,720.
found
faster than simulated annealing, the cost of the proposed combination from tabu search is costlier
than simulated annealing. Hence simulated annealing has a greater success rate than tabu search.
These results can be compared with those of Feng, Liu & Burns [18] who utilised a Genetic
esults
Algorithm to solve this problem and they discovered two non-dominated solutions, 100
non dominated
days/$133,320 and 101 days/$129,320. The best solution found in the current research is very
close to the pareto-optimal front for this problem. The above solutions appear to be an
optimal
improvement when compared with the results of Elbeltagi, Hegazy & Grierson [1
of
[17]. Their
comparative study indicated that the Particle Swarm Optimisation algorithm was best at solving
was
the problem; however the best solution it found was 110 days/ $161,270. The overall results
achieved for each algorithm is presented in a time
time-cost trade-off curve as illustrated in Figure 3.
off
3
Figure 3.Pareto-optimal sets
Figure 3 illustrates the pareto-optimal fronts identified by the different algorithsm, where each
optimal
point on the curve represents a unique time-cost trade-off that is non-dominated. During the initial
.
stages of evaluation, trade-off curve are generated, but they are scattered all over the solution
off
space and does not gather into one region, but as the evaluation progresses the trade-off curve
trade
takes shape. The Genetic Algorithm took 64 iterations to achieve final generation which produces
lgorithm
64
the trade-off curve whereas simulated annealing took 77 iterations and tabu search took 71
off
iterations. An effective way to judge a performance of the algorithm is to ensure that the trade
trade-off
curve is closest to the axis. Hence looking at Figure 3, it is evident that trade-off curve for the
off
genetic algorithm has performed better than that of simulated annealing and tabu search.
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7. CONCLUSIONS
This paper presents the results of applying three metaheuristic search algorithms to a number of
problems that would be typical of those found in the management of software development
projects. All three of the algorithms have the potential to solve scheduling and planning problems,
though the genetic algorithm has performed consistently well when compared against the other
algorithms. Simulated annealing was the second most favourable for this evaluation, and that it is
evident that tabu search is the least favourable choice of algorithm to solve the problems
presented in this paper. This isdifferent to conclusions of Elbeltagi, Hegazy& Grierson [17] who
mentioned that tabu search has been used widely by many researchers to solve not only time-cost
trade-off problem, but many other NP-hard problems.
After finding the trade-off curve, project managers can determine the total cost of the project by
summing up the estimated indirect cost and direct cost from trade-off curve. Using trade-off curve
as the objective function allows for much more efficient evaluation of various other indirect
costs.Future work will investigate the scalability of the approach to significantly larger problems.
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AUTHORS
Andy Connor is a Senior Lecturer in CoLab and has previously worked in the School of
Computing & Mathematical Sciences at AUT. Prior to this he worked as a Senior
Consultant for the INBIS Group on a wide range of systems engineering projects. He has
also worked as a software development engineer and held postdoctoral research positions
at Engineering Design Centres at the University of Cambridge and the University of Bath.
Amit Shah completed hisMasters degree in Computer & Information Science at Auckland University of
Technology, investigating the use of metaheuristic search algorithms applied in the management of
software development projects.