Comparison of Two Swarm Intelligence Optimization Algorithms on the Textual Color Problem for Web Accessibility

Currently, web accessibility is not a major concern of webmasters while creating web sites. For disabled people, it rapidly becomes an obstacle to inclusion in the society. Identifying and circumventing existing barriers constitute an important research topic. In this work, we are concerned with the problem of color accessibility of textual contents in web pages. In many cases, the textual colors of a web page do not respect the minimum constraints defined by recommendations like WCAG 2.0. For example, WCAG 2.0 requires that a minimum difference of brightness, tonality and contrast is ensured. Using the Smart Web Accessibility Platform, we try to transform the colors using a client-side HTTP proxy the best possible while retaining a reasonable access time for the web content. To solve the textual color problem for accessibility, we adapt two swarm intelligence based optimization methods (ABC and API) and we hybridize them with a line search.

[1]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[2]  Nicolas Monmarché,et al.  Web Page Textual Color Contrast Compensation for CVD Users Using Optimization Methods , 2014, J. Math. Model. Algorithms Oper. Res..

[3]  Sebastien Aupetit,et al.  Annotation Tool for the Smart Web Accessibility Platform , 2014, ICCHP.

[4]  Mohamed Slimane,et al.  Automatic Color Improvement of Web Pages with Time Limited Operators , 2012, ICCHP.

[5]  Dominique Fresneau.  Biologie et comportement social d'une fourmi ponérine néotropicale (Pachycondyla Apicalis) , 1994 .

[6]  Mohamed Slimane,et al.  Improving Web Accessibility for Dichromat Users through Contrast Preservation , 2012, ICCHP.

[7]  E Giraud-Baro,et al.  Loi du 11 février 2005 pour l'égalité des droits et des chances, la participation et la citoyenneté des personnes handicapées , 2009 .

[8]  D. Fresneau,et al.  Individual foraging and path fidelity in a ponerine ant , 1985, Insectes Sociaux.

[9]  Nicolas Monmarché,et al.  An Evolutionary Approach to Contrast Compensation for Dichromat Users , 2013, Artificial Evolution.

[10]  Nicolas Monmarché,et al.  Algorithmes de fourmis artificielles : applications à la classification et à l'optimisation. (Artificial ant based algorithms applied to clustering and optimization problems) , 2000 .

[11]  Wolfgang L. Zagler,et al.  Computers Helping People with Special Needs, 12th International Conference, ICCHP 2010, Vienna, Austria, July 14-16, 2010, Proceedings, Part II , 2010, ICCHP.

[12]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .