Exploiting antipheromone in ant colony optimisation for interactive search-based software design and refactoring

Simons, Chris; Smith, Jim

Abstract

Preventing user-fatigue in interactive meta-heuristic search places as great an emphasis on efficiency as it does on ef- fectiveness. Engagement may also be boosted if the system provides a sense of “responsiveness” - for example, avoiding unpopular solutions as well as exploiting preferred ones. In this paper we explore one possible way of achieving these goals using the concept of “anti-pheromones” in different forms of Ant Colony Optimisation. Taking search-based software design and refactoring as a case study, we use exten- sive offline experiments to investigate differences of timescale and method for applying anti-pheromones. Results confirm our predictions that most combinations are in fact counter- productive. However, applying high levels of anti-pheromone, only in the initial stages of a run, can rapidly steer the search away from unproductive regions, reducing the number of evaluations required by up to 20% without compromising solution fitness.

Citation

Simons, C., & Smith, J. (2016, July). Exploiting antipheromone in ant colony optimisation for interactive search-based software design and refactoring. Poster presented at ACM-SIGEVO Genetic and Evolutionary Computation Conference, GECCO ’16, Denver, CO, USA

• Presentation Conference Type: Poster
• Conference Name: ACM-SIGEVO Genetic and Evolutionary Computation Conference, GECCO ’16
• Conference Location: Denver, CO, USA
• Start Date: Jul 20, 2016
• End Date: Jul 24, 2016
• Acceptance Date: Mar 20, 2016
• Publication Date: Jan 1, 2016
• Deposit Date: May 19, 2016
• Publicly Available Date: May 17, 2017
• Peer Reviewed: Peer Reviewed
• Pages: 143-144
• Keywords: search based software engineering, ant colony optimisation
• Public URL: https://uwe-repository.worktribe.com/output/923267
• Publisher URL: http://dx.doi.org/10.1145/2908961.2909018
• Additional Information: Title of Conference or Conference Proceedings : ACM-SIGEVO Genetic and Evolutionary Computation Conference, GECCO ’16