Multi-objective particle swarm optimisation: methods and applications (2014)
Thesis
Al Moubayed, N. (2014). Multi-objective particle swarm optimisation: methods and applications. (Thesis). Robert Gordon University. Retrieved from https://durham-repository.worktribe.com/output/1618029

Solving real life optimisation problems is a challenging engineering venture. Since the early days of research on optimisation it was realised that many problems do not simply have one optimisation objective. This led to the development of multi-obje... Read More about Multi-objective particle swarm optimisation: methods and applications.

Face-Based Automatic Personality Perception (2014)
Conference Proceeding
Al Moubayed, N., Vazquez-Alvarez, Y., McKay, A., & Vinciarelli, A. (2014). Face-Based Automatic Personality Perception. In Proceedings of the 22nd ACM international conference on Multimedia - MM '14, November 03–07, 2014, Orlando, FL, USA (1153-1156). https://doi.org/10.1145/2647868.2655014

Automatic Personality Perception is the task of automatically predicting the personality traits people attribute to others. This work presents experiments where such a task is performed by mapping facial appearance into the Big-Five personality trait... Read More about Face-Based Automatic Personality Perception.

D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces (2014)
Journal Article
Al Moubayed, N., Petrovski, A., & McCall, J. (2014). D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces. Evolutionary Computation, 22(1), 47-77. https://doi.org/10.1162/evco_a_00104

This paper improves a recently developed multi-objective particle swarm optimizer () that incorporates dominance with decomposition used in the context of multi-objective optimization. Decomposition simplifies a multi-objective problem (MOP) by trans... Read More about D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces.