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Multi-criteria website optimization using multi-objective ACO

机译：使用多目标ACO的多标准网站优化

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The rapid growth of Internet has led to an unprecedented rise to e-commerce. Organizations, while exploiting this opportunity, at the same time are themselves facing stiff competition in order to sustain and rise in this dynamic online market. They are investing a lot for improving their online presence through an effective website design. Such website designs need to address the two-fold challenge of improving the user's navigation experience, even while simultaneously increasing the turnover of the organization. Optimal configuration of web items entails optimization of key criteria like minimization of download time, maximization of visualization and maximizing the potential sale of products or services available through the underlying website configuration, among others. This multi-criteria website optimization (MCWSO) problem has already been formulated as an aggregated weighted sum of the three objectives and solved using the genetic algorithm (GA). It is impractical to have aprior knowledge of the weights for the three objectives, as varied classes of users have different preferences for different criteria. Thus, there is a need to simultaneously optimize the three objectives in order to achieve trade-off solutions, having wider spreads on the Pareto front. Accordingly in this paper, a Pareto based multi-objective ant colony optimization (ACO) based MCWSO algorithm, that achieves such trade-off solutions, has been proposed. Experimental results show that the multi-objective ACO based MCWSO algorithm, in comparison to the GA based MCWSO algorithm, is able to generate Top-K web object sequences that are capable of catering to varied classes of users.
机译：互联网的快速增长导致了前所未有的增长，以电子商务。组织，同时利用这个机会，同时它们本身面临着激烈的竞争，以维持和上升在这个充满活力的在线市场。他们投入了很多关于通过有效的网站设计改善他们的在线状态。这样的网站设计需要解决改善用户的导航体验的双重挑战，甚至同时提高企业的营业额。网页的项目优化配置需要的像的下载时间最小化，可视化的最大化，并通过下面的网站配置最大化的潜在销售产品或提供服务等关键指标的优化。这种多标准的网站优化（MCWSO）的问题已经被配制成的三个目标的合计加权和，并用遗传算法（GA）解决。这是不切实际的权重为三个目标的aprior知识，为用户的不同类别有不同的标准有不同的偏好。因此，有必要同时优化以实现折衷的解决方案的三个目标，具有上帕累托前差扩大。因此在本文中，基于MCWSO算法基于帕累托多目标蚁群优化（ACO），即达到这样的折衷解决方案，已经提出。实验结果表明，基于多目标ACO MCWSO算法，相比于基于GA MCWSO算法，能够产生，其能够迎合不同类别的用户中的前K个web对象序列。

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