For solving users' disorientation problems when using Web-based systems, there is an important issue to understand why they cause such problems. To this end, there is a need to investigate the relationship between users' characteristics and their disorientation problems. However, when facing this challenge, it is difficult to identify which users' characteristics may play important factors or how their characteristics interact with each other to influence their disorientation problems. Thus, this paper tends to propose an automatic architecture for solving this issue. More specifically, this study proposes a multiplestrategy evolutionary neural fuzzy network (MSE-NFN) to not only provides an efficiency way to automatically identify users' disorientation problems but also investigate which users' characteristics greatly influence their disorientation problems. The results indicate that users' experience of using Internet, experience of using navigation tools and different levels of prior knowledge are influential factors to affect their disorientation problems. Moreover, it also demonstrates that the proposed architecture (MSE-NFN) outperform than other existing evolutionary methods. Based on the results, a framework is conducted, which can be used to automatically identify users' disorientation problems when developing the personalized Web-based systems.
Abstract-The particle swarm optimizer (PSO) is a populationbased optimization technique that can be widely utilized to many applications. The cooperative particle swarm optimization (CPSO) applies cooperative behavior to improve the PSO on finding the global optimum in a high-dimensional space. This is achieved by employing multiple swarms to partition the search space. However, independent changes made by different swarms on correlated variables will deteriorate the performance of the algorithm. This paper proposes a separability detection approach based on covariance matrix adaptation to find non-separable variables so that they can previously be placed into the same swarm to address the difficulty that the original CPSO encounters.
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