A Hybrid Swarm Based Approach in Addressing University Timetabling Problems

This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carter's examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

An investigation of vision problems and the vision care system in Rural China

This paper examines the prevalence of vision problems and the accessibility to and quality of vision care in rural China. We obtained data from 4 sources: 1) the National Rural Vision Care Survey; 2) the Private Optometrists Survey; 3) the County Hospital Eye Care Survey; and 4) the Rural School Vision Care Survey. The data from each of the surveys were collected by the authors during 2012. Thirty-three percent of the rural population surveyed self-reported vision problems. Twenty-two percent of subjects surveyed had ever had a vision exam. Among those who self-reported having vision problems, 34% did not wear eyeglasses. Fifty-four percent of those with vision problems who had eyeglasses did not have a vision exam prior to receiving glasses. However, having a vision exam did not always guarantee access to quality vision care. Four channels of vision care service were assessed. The school vision examination program did not increase the usage rate of eyeglasses. Each county-hospital was staffed with three eye-doctors having one year of education beyond high school, serving more than 400,000 residents. Private optometrists often had low levels of education and professional certification. In conclusion, our findings shows that the vision care system in rural China is inadequate and ineffective in meeting the needs of the rural population sampled.

A Hybrid Imperialist Approach in Solving University Timetabling Problems. Information Sciences. Vol. 283, 1-21

Generating timetables for an institution is a challenging and time consuming task due to different demands on the overall structure of the timetable. In this paper, a new hybrid method which is a combination of a great deluge and artificial bee colony algorithm (INMGD-ABC) is proposed to address the university timetabling problem. Artificial bee colony algorithm (ABC) is a population based method that has been introduced in recent years and has proven successful in solving various optimization problems effectively. However, as with many search based approaches, there exist weaknesses in the exploration and exploitation abilities which tend to induce slow convergence of the overall search process. Therefore, hybridization is proposed to compensate for the identified weaknesses of the ABC. Also, inspired from imperialist competitive algorithms, an assimilation policy is implemented in order to improve the global exploration ability of the ABC algorithm. In addition, Nelder–Mead simplex search method is incorporated within the great deluge algorithm (NMGD) with the aim of enhancing the exploitation ability of the hybrid method in fine-tuning the problem search region. The proposed method is tested on two differing benchmark datasets i.e. examination and course timetabling datasets. A statistical analysis t-test has been conducted and shows the performance of the proposed approach as significantly better than basic ABC algorithm. Finally, the experimental results are compared against state-of-the art methods in the literature, with results obtained that are competitive and in certain cases achieving some of the current best results to those in the literature.

Dynamic State Estimation of Power Systems with Quantization Eects: A Recursive Filter Approach

In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.

Systematic Review of the Measurement Properties of Tools Used to Measure Behaviour Problems in Young Children with Autism

Background
Behaviour problems are common in young children with autism spectrum disorder (ASD). There are many different tools used to measure behavior problems but little is known about their validity for the population.

Objectives
To evaluate the measurement properties of behaviour problems tools used in evaluation of intervention or observational research studies with children with ASD up to the age of six years.

Methods
Behaviour measurement tools were identified as part of a larger, two stage, systematic review. First, sixteen major electronic databases, as well as grey literature and research registers were searched, and tools used listed and categorized. Second, using methodological filters, we searched for articles examining the measurement properties of the tools in use with young children with ASD in ERIC, MEDLINE, EMBASE, CINAHL, and PsycINFO. The quality of these papers was then evaluated using the COSMIN checklist.

Results
We identified twelve tools which had been used to measure behaviour problems in young children with ASD, and fifteen studies which investigated the measurement properties of six of these tools. There was no evidence available for the remaining six tools. Two questionnaires were found to be the most robust in their measurement properties, the Child Behavior Checklist and the Home Situations Questionnaire—Pervasive Developmental Disorders version.

Conclusions
We found patchy evidence on reliability and validity, for only a few of the tools used to measure behaviour problems in young children with ASD. More systematic research is required on measurement properties of tools for use in this population, in particular to establish responsiveness to change which is essential in measurement of outcomes of intervention.

PROSPERO Registration Number

CRD42012002223