Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple conflicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated solutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark problems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs

Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems

Impact analysis of crossovers in a multi-objective evolutionary algorithm

Multi-objective optimization has become mainstream because several real-world problems are naturally posed as a Multi-objective optimization problems (MOPs) in all fields of engineering and science. Usually MOPs consist of more than two conflicting objective functions and that demand trade-off solutions. Multi-objective evolutionary algorithms (MOEAs) are extremely useful and well-suited for solving MOPs due to population based nature. MOEAs evolve its population of solutions in a natural way and searched for compromise solutions in single simulation run unlike traditional methods. These algorithms make use of various intrinsic search operators in efficient manners. In this paper, we experimentally study the impact of different multiple crossovers in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework and evaluate its performance over test instances of 2009 IEEE congress on evolutionary computation (CEC?09) developed for MOEAs competition. Based on our carried out experiment, we observe that used variation operators are considered to main source to improve the algorithmic performance of MOEA/D for dealing with CEC?09 complicated test problems

MPICH-G2: A Grid-Enabled Implementation of the Message Passing Interface

Application development for distributed computing "Grids" can benefit from tools that variously hide or enable application-level management of critical aspects of the heterogeneous environment. As part of an investigation of these issues, we have developed MPICH-G2, a Grid-enabled implementation of the Message Passing Interface (MPI) that allows a user to run MPI programs across multiple computers, at the same or different sites, using the same commands that would be used on a parallel computer. This library extends the Argonne MPICH implementation of MPI to use services provided by the Globus Toolkit for authentication, authorization, resource allocation, executable staging, and I/O, as well as for process creation, monitoring, and control. Various performance-critical operations, including startup and collective operations, are configured to exploit network topology information. The library also exploits MPI constructs for performance management; for example, the MPI communicator construct is used for application-level discovery of, and adaptation to, both network topology and network quality-of-service mechanisms. We describe the MPICH-G2 design and implementation, present performance results, and review application experiences, including record-setting distributed simulations.Comment: 20 pages, 8 figure

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA

Developing an understanding of the nature of accessibility and usability problems blind students face in web-enhanced instruction environments

The central premise of this research is that blind and visually impaired (BVI) people cannot use the Internet effectively due to accessibility and usability problems. Use of the Internet is indispensable in today's education system that relies on Web-enhanced instruction (WEI). Therefore, BVI students cannot participate effectively in WEI. Extant literature recognizes that non-visual Web interaction is inherently challenging. However, it does not explain where, how and why BVI students face accessibility and usability problems in performing academic tasks in WEI. This knowledge is necessary to adequately inform the development of interventions that improve the functional and academic outcomes of BVI students in WEI. The purpose of this doctoral research is to understand the nature of accessibility and usability problems BVI students face in WEI environments. It adopts a novel user-centered, task-oriented, cognitive approach to develop an in-depth, contextually-situated, observational and experiential knowledge of these problems. The context of WEI experience under investigation is an online exam over a typical course management system. Research design is a qualitative field study that involves a multimethod evaluation of the WEI environment. The core component of this multimethod evaluation is BVI students' assessment of the WEI environment. This is triangulated through assessments made by WCAG (Web Content Accessibility Guidelines) and Web developers. The BVI student assessment employs an integrated problem solving model, in combination with verbal protocol analysis, to identify and understand where, how and why BVI students face a problem in completing the exam. The WCAG assessment employs automated accessibility testing and WCAG textual analysis to identify interface objects that violate accessibility standards and characterizes a problem. The Web developer assessment involves open-ended interviews to identify the source of a problem. Results show that the WEI environment consisted of innumerable interface objects that violated WCAG's standards on Web accessibility and usability. BVI participants faced many accessibility and usability problems that posed significant challenges completing the online exam. These problems fall into six major problem types as described below: 1. Confusion while navigating across WEI environment due to its frame-based page structure without unique frame names; 2. Susceptibility to submitting incomplete work when a new question page does not provide location and contextual information; 3. Difficulty understanding how to submit work when the selection controls for multiple option questions lack a consistent keyboard navigation procedure; 4. Inability to negotiate security information pop-up when the WEI environment uses an alert dialogue box; 5. Ambiguity in essay-type question page that lack meaningful labels for interface objects, including text area and text formatting toolbar; 6. Vulnerability of losing work when Backspace behaves as browser's Back button inside text area. This doctoral research contributes in three ways. It fills the knowledge gap about the nature of problems BVI students face in Web interactions for academic tasks. This kind of knowledge is necessary to determine accessibility and usability requirements for WEI. Another contribution is a set of mental model representations that explicate the thought processes of BVI students. Such representations are useful in developing user instruction and design of more accessible and usable Web sites. A third contribution is a user-centered, task-based, cognitive and multi-method approach to evaluate Web accessibility and usability

Motivations underlying career decision-making activities : the career decision-making autonomy scale (CDMAS)

The purpose of the present research was to develop and validate a measure of motivation toward career decision-making activities, the Career Decision-Making Autonomy Scale (CDMAS). The CDMAS is designed to assess the constructs of intrinsic motivation, identified regulation, introjected regulation, and external regulation. A longitudinal study was used to develop and validate the CDMAS. Overall, results show that the CDMAS is composed of four internally consistent factors. The construct validity of the scale is also supported by (a) a quasi-simplex pattern of correlations, (b) correlations with personality variables and vocational constructs, and (c) convergent and divergent correlations. In sum, the CDMAS represents a valid self-report measure of intrinsic motivation, identified regulation, introjected regulation, and external regulation toward career decision-making activitie

An Architecture for Dynamic Meta-Level Process Control for Model-Based Troubleshooting

There are numerous methods used for troubleshooting devices. Each method has certain domains, knowledge requirements, and assumptions required for it to perform well. However, oftentimes no one method by itself is sufficient to completely solve a troubleshooting problem. Therefore, an architecture is required to control the combined use of many problem solving methods. The combination of multiple problem solving methods makes the troubleshooting process more robust in terms of device domains that can be dealt with and quality of diagnoses produced. Troubleshooting has two tasks: diagnosis and problem resolution. This research provides an architecture that allows dynamic method selection during diagnosis. Dynamic method selection factors the current state of the diagnosis process along with other method parameters to determine which method to use to advance the diagnosis process. The architecture was developed by combining themes from diagnosis research that focused on dynamic multimethod diagnosis and its control. This work has produced several results. It provides an architecture to organize the methods and a basis for making control decisions concerning method use during diagnosis. It identifies a generous number of methods useful to perform diagnosis. It identifies the knowledge these methods require