Recent publications suggest that resolving multidimensional tasks where optimisation parameters are hundreds and more faces unusual computational limitation. In the same time optimisation algorithms, which perform well on tasks with low number of dimensions, when are applied to high dimensional tasks require infeasible period of time and computational resources. This article presents a novel investigation on Differential Evolution and Particle Swarm Optimisation with enhanced adaptivity and Free Search applied to 200 dimensional versions of three scalable, global, real-value, numerical tests, which optimal values are dependent on dimensions number and virtually unknown for variety of dimensions. The aim is to: (1) identify computational limitations which numerical methods could face on 200 dimensional tests; (2) identify relations between test complexity and period of time required for tests resolving; (3) discover unknown optimal solutions; (4) identify specific methods’ peculiarities which could support the performance on high dimensional tasks. Experimental results are presented and analysed.
|Lecture Notes in Computer Science
|Numerical Methods & Applications
|20/08/14 → 24/08/14