Methodology for shape optimization of ultrasonic amplifier using genetic algorithms and simplex method

Designing devices for ultrasonic vibration applications is mostly done by intuitively adjusting the geometry to obtain the desired mode of vibration at a specific operating frequency. Recent studies have shown that with optimization methods, new devices with improved performance can be easily found. In this investigation, a new methodology for designing an ultrasonic amplifier through shape optimization using genetic algorithms and simplex method with specific fitness functions is presented. Displacements at specific functional areas, main functionality, and mode frequency are considered to determine the properties of an individual shape to meet the stated criteria. Length, diameter, position of mountings, and further specific geometric parameters are set up for the algorithm search for an optimized shape. Beginning with genetic algorithms, the basic shape fitting the stated requirements is found. After that the simplex method further improves the found shape to most appropriately minimize the fitness function. At the end, the fittest individual is selected as the final solution. Finally, resulting shapes are experimentally tested to show the effectiveness of the methodology.     

A survey study of critical success factors in agile software projects

While software is so important for all facets of the modern world, software development itself is not a perfect process. Agile software engineering methods have recently emerged as a new and different way of developing software as compared to the traditional methodologies. However, their success has mostly been anecdotal, and research in this subject is still scant in the academic circles. This research study was a survey study on the critical success factors of Agile software development projects using quantitative approach.

Based on existing literature, a preliminary list of potential critical success factors of Agile projects were identified and compiled. Subsequently, reliability analysis and factor analysis were conducted to consolidate this preliminary list into a final set of 12 possible critical success factors for each of the four project success categories – Quality, Scope, Time, and Cost.

A survey was conducted among Agile professionals, gathering survey data from 109 Agile projects from 25 countries across the world. Multiple regression techniques were used, both at the full regression model and at the optimized regression model via the stepwise screening procedure. The results revealed that only 10 out of 48 hypotheses were supported, identifying three critical success factors for Agile software development projects: (a) Delivery Strategy, (b) Agile Software Engineering Techniques, and (c) Team Capability.

Limitations of the study are discussed together with interpretations for practitioners. To ensure success of their projects, managers are urged to focus on choosing a high-caliber team, practicing Agile engineering techniques and following Agile-style delivery strategy.     

Two-dimensional packing problems using genetic algorithms

This paper presents a technique for applying genetic algorithms for the two-dimensional packing problem. The approach is applicable to not only convex shaped objects, but can also accommodate any type of concave and complex shaped objects including objects with holes. In this approach, a new concept of a two-dimensional genetic chromosome is introduced. The total layout space is divided into a finite number of cells for mapping it into this 2D genetic algorithm chromosome. The mutation and crossover operators have been modified and are applied in conjunction with connectivity analysis for the objects to reduce the creation of faulty generations. A new feature has been added to the Genetic Algorithm (GA) in the form of a new operator called compaction. Several examples of GA-based layout are presented.     

An investigation of genetic algorithms for the optimization of multi-objective fisheries bioeconomic models

The use of genetic algorithms (GA) for optimization problems offers an alternative approach to the traditional solution methods. GA follow the concept of solution evolution, by stochastically developing generations of solution populations using a given fitness statistic, for example the achievement function in goal programs. They are particularly applicable to problems which are large, non-linear and possibly discrete in nature, features that traditionally add to the degree of complexity of solution. Owing to the probabilistic development of populations, GA do not distinguish solutions, e.g. local optima from other solutions, and therefore cannot guarantee optimality even though a global optimum may be reached. In this paper, a non-linear goal program of the North Sea demersal fisheries is used to develop a genetic algorithm for optimization. Comparisons between the GA approach and traditional solution methods are made, in order to measure the relative effectiveness. General observations of the use of GA in multi-objective fisheries bioeconomic models, and other similar models, are discussed.     

Fuzzy assembly line balancing using genetic algorithms

In this paper, we implement genetic algorithms to synthesis fuzzy assembly line balancing problem which is well-known as a NP-hard problem. The genetic operators concerned with the feasibility of chromosomes will be discussed, and its performance will be shown with a numerical example.     

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.     

A genetic algorithm with memory for mixed discrete-continuous design optimization

This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both discrete and continuous design variables, multivariate approximation of the fitness function in terms of several continuous design variables, and localized search based on the multivariate approximation. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.     

Equilibrium analysis of multibody dynamic systems using genetic algorithm in comparison with constrained and unconstrained optimization techniques

The present paper describes a set of procedures for the solution of nonlinear equilibrium problems in complex multibody systems. To find the equilibrium position of the system, six different optimization algorithms are used to minimize the total potential energy (TPE) of the system and compared with respect to accuracy and efficiency. A computer program is developed to evaluate the equality constraints and objective function of a general multibody dynamic system to find the equilibrium condition. It is seen that the indirect methods have better results and converge faster. Also it is shown that the genetic algorithm (GA) results in a global optimum while the other methods converge to a local optimum.     

神经网络、遗传算法在发酵优化软件平台设计中的应用

本课题设计了基于DDE的VB、Excel和Matlab的发酵过程优化软件平台，并将该软件平台应用到多粘菌素发酵过程PH值的寻优。实践表明：该优化软件平台能够确定发酵过程被控参数的优化轨线，为优化控制提供一个主要目标。     

Fuzzy classifier design using genetic algorithms

A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the training data using GAs. This is done by introducing new representation schemes for fuzzy membership functions and fuzzy rules. An effectiveness measure for fuzzy rules is developed that allows for systematic addition or deletion of rules during the GA optimization process. A clustering method is utilized for generating new rules to be added when additions are required. The performance of the classifier is tested on two real-world databases (Iris and Wine) and a simulated Gaussian database. The results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules. The performance is also compared to other fuzzy classifiers tested on the same databases.     

Open pit mine production schedule optimization using a hybrid of maximum-flow and genetic algorithms

Production scheduling is a critical activity for the long-term production planning of open pit mining operations. It deals with the effective management of resources and maximizes cash flows to generate higher profits over the life of a mine. Production scheduling problems determine that blocks be mined and processed over a number of periods subjected to mining and processing constraints, which makes the problem more complex. The complexity is further increased due to the uncertainty in the input parameters. In this study, the maximum flow algorithm with a genetic algorithm is used to generate the long-term production schedule. The graph structure for maximum flow is created for multiple periods under uncertainty, and the flow in the arcs is controlled by a genetic algorithm to develop a production schedule. Numerical results for realistic instances are provided to indicate the efficiency of the solutions.     

A computerized causal forecasting system using genetic algorithms in supply chain management

Forecasting activities are widely performed in the various areas of supply chains for predicting important supply chain management (SCM) measurements such as demand volume in order management, product quality in manufacturing processes, capacity usage in production management, traffic costs in transportation management, and so on. This paper presents a computerized system for implementing the forecasting activities required in SCM. For building a generic forecasting model applicable to SCM, a linear causal forecasting model is proposed and its coefficients are efficiently determined using the proposed genetic algorithms (GA), canonical GA and guided GA (GGA). Compared to canonical GA, GGA adopts a fitness function with penalty operators and uses population diversity index (PDI) to overcome premature convergence of the algorithm. The results obtained from two case studies show that the proposed GGA provides the best forecasting accuracy and greatly outperforms the regression analysis and canonical GA methods. A computerized system was developed to implement the forecasting functions and is successfully running in real glass manufacturing lines.     

Formal model for assigning human resources to teams in software projects

Context

Human resources play a critical role in software project success. However, people are still the least formalized factor in today’s process models. Generally, people are assigned to roles and project teams are formed on the basis of project leaders’ experience of people, constraints (e.g. availability) and skill requirements. Yet this process has to take multiple factors into account. Few works in the literature model this process. Most of these are informal proposals focusing on the individual assignment of people to project tasks and do not consider other aspects like team formation as a whole.

Objective

In this paper we formulate a formal model for assigning human resources to software project teams. Additionally, we describe the key results of the knowledge management process enacted to output the elements of the model.

Method

The model elements were identified using the Delphi expert consultation method and applying psychological tests. The proposed model was implemented in a software tool and validated on two software development organization assignment scenarios.

Results

We built a formal model for the process of assigning human resources to software project teams. This model takes into account as many factors as possible and aids the assignment of individuals to project roles, as well as the formation of the team as a whole.We found that the rules that were identified to form software development project teams are useful. From the tests we found that model implementation was feasible (all the executions of the implemented problem-solving algorithms output feasible solutions in response times that can be considered as acceptable).

Conclusion

Using the Delphi method we were able to propose software project roles and competences. Psychological tests and data mining tools identified useful rules for forming software project teams. These were used to build a formal model. This model was built into a tool that returns role assignments in acceptable response times. This decision support tool helps managers assign people to roles and to form teams. Using the tool, project leaders can flexibly evaluate different team make-ups, taking into account several factors, as well as different constraints and objectives.     

Genetic algorithms for video segmentation

The current paper presents a new genetic algorithm (GA)-based method for video segmentation. The proposed method is specifically designed to enhance the computational efficiency and quality of the segmentation results compared to standard GAs. The segmentation is performed by chromosomes that independently evolve using distributed genetic algorithms (DGAs). However, unlike conventional DGAs, the chromosomes are initiated using the segmentation results of the previous frame, instead of random values. Thereafter, only unstable chromosomes corresponding to moving object parts are evolved by crossover and mutation. As such, these mechanisms allow for effective solution space exploration and exploitation, thereby improving the performance of the proposed method in terms of speed and segmentation quality. These advantages were confirmed based on experiments where the proposed method was successfully applied to both synthetic and natural video sequences.     

Open source software success: Measures and analysis

Despite a growing body of research on OSS production, much remains to be learned. One important issue concerns the measures of OSS project success and its determinants. In this paper, we empirically study the determinants of OSS success as measured by the number of subscribers and developers working on an OSS project. Furthermore, we demonstrate that our model forecasts these success measures more accurately as compared to a naive model.We find that OSS projects that develop software to work on Windows/UNIX/Linux operating systems, and developed using C or its derivative languages experience larger increase in subscribers and attract more developers than projects that do not have these characteristics. OSS projects with semi-restrictive licenses have fewer subscribers and attract fewer developers. Interestingly, OSS projects that accept financial donations and are targeted at IS/IT professionals have more subscribers than others, although these characteristics do not affect the developer base. The number of subscribers and developers increases with the age of the OSS project. Finally, the impact of developers on subscribers and subscribers on developers is positive and significant.     

On-line free-flight path optimization based on improved genetic algorithms

Free flight (FF) is the ideal strategy of current investigations on air traffic management systems, where an on-line flight path optimization algorithm is of top importance. This paper proposes an innovative algorithm with potential real-time properties for FF path optimization, by using an improved genetic algorithm (GA). Two kinds of mathematical models for the on-line flight path optimization problem are proposed to cover the near and far future applications. Several improvements are introduced to the GA to speed up its convergence as well as to improve performance. Simulation results show that the new algorithm is effective and has potential to solve the on-line FF path optimization problem in real time.     

Biological-based genetic algorithms for optimized disaster response resource allocation

An effective disaster response requires rapid coordination of existing resources, which can be considered a resource optimization problem. Genetic algorithms (GAs) have been proven effective for solving optimization problems in various fields. However, GAs essentially use generation succession to search for optimal solutions. Therefore, their use of reproduction, crossover, and mutation operations may exclude optimal chromosomes during generation succession and prevent full use of previous search experience. Meanwhile, premature convergence caused by inadequate diversity of chromosome populations limits the search to a local optimum. Genetic algorithms also incur high computational costs. The biological-based GAs (BGAs) proposed in this study address these problems by including mechanisms for elite reserve areas, nonlinear fitness value conversion, and migration. This study performed experimental simulations to compare BGAs with immune algorithms (IAs) and GAs in terms of effectiveness for allocating disaster refuge site staff and for planning relief supply distribution. The simulation results show that, compared to other methods, BGAs can compute optimal solutions faster. Therefore, they provide a more useful reference when performing the decision-making needed to solve disaster response resource optimization problems.     

An optimization framework for “build-or-buy” decisions in software architecture

Building a software architecture that meets functional requirements is a quite consolidated activity, whereas keeping high quality attributes is still an open challenge. In this paper we introduce an optimization framework that supports the decision whether to buy software components or to build them in-house upon designing a software architecture. We devise a non-linear cost/quality optimization model based on decision variables indicating the set of architectural components to buy and to build in order to minimize the software cost while keeping satisfactory values of quality attributes. From this point of view, our tool can be ideally embedded into a Cost Benefit Analysis Method to provide decision support to software architects. The novelty of our approach consists in building costs and quality attributes on a common set of decision variables related to software development. We start from a special case of the framework where the quality constraints are related to the delivery time and the product reliability, and the model solution also devises the amount of unit testing to be performed on built components. We generalize the framework formulation to represent a broader class of architectural cost-minimization problems under quality constraints, and discuss advantages and limitations of such approach.     

Applying a genetic algorithm to the optimization of composite patches

This paper describes the application of genetic algorithms (GAs) to the optimization of a composite patch bonded on a metallic structure. The objective is to reduce the stress level in a given area under some constraints such as a maximum surface of the patch and some forbidden zones which cannot be covered by the patch. GAs are presently used for optimizing ply orientations of the stacking sequence as well as for the location and the shape of the patch which is modelled with a spline function. The design variables are ply orientations and coordinates of interpolation points which define a closed plane spline curve. Stress field calculations in the structure are carried out using the Ansys finite element package. The structure considered in this study is an aluminium plate with a circular hole around which the stress level must be reduced by bonding the composite patch. Shapes and ply stacking sequences resulting from the optimization procedure enable a local reinforcement in the neighbourhood of the patch and also a long-distance effect which consists in a deviation of the stress flow adjoining the area over which the stress level must be reduced.