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University course timetable problem (UCTP) is one of the problems on which many researches have been conducted over the years because of its importance in academic institutions. A nature-inspired metaheuristic optimization algorithm, Flower Pollination Algorithm (FPA) has been adapted, so-called Adapted FPA (AFPA), to cope with UCTP in the previous work. However, AFPA suffers from the stagnation problem because of the non-diversity in the population. To improve the diversity of the population, this work introduces new Hybrid FPA with two variants: JFPA provided the Jaccard index to determine similarities among categorical data and the greedy selection mechanism to improve the selection of the random solution, and DFPA applied the navigational characteristics of the Dragonfly Algorithm (DA) to help in the neighborhood relationship. The results in this study indicate that the proposed algorithms have better exploration ability and fast convergence rate in comparison; JFPA outperforms AFPA in 3 out of 4 datasets for both small and large datasets, and DFPA outperforms AFPA and GA in all datasets while it outperforms PSO in 3 out of 4 small datasets and 2 complex large datasets.

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When developing applications, User Interfaces (UI) are considered as an important and integral component of any application.  With badly designed UI, users are less likely to use the application, leading to low adoption rates and is not desirable in any application development setting.  The process of developing good intuitive and stream-lined UI for users is complex, that requires many processes and experts from many fields to contribute.  When evaluating potential UI designs, there are many attributes and features that could be examined either in a qualitative and/or quantitative standpoint.  The Updated Goals, Operators, Methods, and Selection Rules (GOMS) model is an approach that has been used in the area.  With the Keystroke Level (KLM) extension, it is possible to quantitatively estimate the time requirement or efficiency of UI for completing different tasks with minimal effort, and has been adopted in many GUI improvement projects.  Due to the usefulness, extensions to the GOMS model had been proposed over the years including extensions to account for motion control interfaces.  Though the GOMS model has been useful for quantitatively evaluating UI design, the main input device of modern smartphones and tablets are touch screens, which are different in nature when compared to traditional computer inputs.  The differences leads to the point that GOMS model with KLM is ill-suited for touch screen based applications.  This research paper addresses the issue by proposing extensions to the GOMS model to account for UI that are based on touch screen input devices.

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