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首页> 外文会议>Third IEEE International Conference on Intelligent Networking and Collaborative Systems >Exploring Fish School Algorithm for Improving Turnaround Time: An Experience of Content Retrieval
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Exploring Fish School Algorithm for Improving Turnaround Time: An Experience of Content Retrieval

机译：探索改进周转时间的鱼群算法：内容检索的经验

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In distributed e-learning paradigm, learning pedagogy demands different content retrieval methodologies after reaching certain boundary of learning. Hence, the learners are expected, to retrieve the contents and they need to improvise at substantially faster rate. The overall learning process converges into a finite time usage and they will return to the same point of access point. The present paper proposes an innovative fish school algorithm to minimize the turnaround time of content retrieval of learner so as to improve learning efficiency. The deployment of fish school contemplates either Prey (the fish perceives the concentration of food in water to determine the movement by vision or sense and then chooses the tendency) swarm ( the fish will assemble in groups naturally in the moving process, which is a kind of living habits in order to guarantee the existence of the colony and avoid dangers) or Follow(in the moving process of the fish swarm, when a single fish or several fish find food, the neighborhood partners will trail and reach the food quickly). In the present problem of content retrieval, these verticals of Fish school are referred to quantify the symbol definition, constraint strategy and stopping criteria for improving turnaround time for the content. The Fish school has the better iterative potential over the other conventional derivative free optimization techniques e.g. Particle Swarm Optimization and Ant Colony Algorithm, and moreover the proposed algorithm can be well interfaced with web portal of e-learning content retrieval. Couples of characteristic results have been included to support the anomalies as the improvement of turnaround time.
机译：在分布式电子学习范式中，学习教学法在达到一定的学习边界后需要不同的内容检索方法。因此，期望学习者检索内容，并且他们需要以显着更快的速度即兴创作。整个学习过程收敛为有限的时间使用量，它们将返回到接入点的同一点。本文提出了一种创新的鱼群算法，以最小化学习者内容检索的周转时间，从而提高学习效率。鱼群的部署考虑到捕食者（鱼会感知水中食物的浓度，从而通过视觉或感觉来决定运动，然后选择趋势）蜂拥而至（鱼在运动过程中会自然地成群聚集，这是一种（为了保证种群的存在并避免危险）的生活习惯或跟随（在鱼群的移动过程中，当一条鱼或几条鱼找到食物时，邻居伙伴将迅速追踪并到达食物）。在当前的内容检索问题中，Fish学校的这些垂直领域被用来量化符号定义，约束策略和停止标准，以缩短内容的周转时间。与其他传统的无导数优化技术相比，Fish School具有更好的迭代潜力。粒子群优化和蚁群算法，并且所提出的算法可以很好地与电子学习内容检索的门户网站接口。包括了几个特征结果，以支持异常情况，从而缩短了周转时间。

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