1. A new evolutionary search strategy for global optimization of high-dimensional problems
- Author
-
Chu, W, Gao, X, and Sorooshian, S
- Subjects
Global optimization ,High-dimensional ,Principal components analysis ,Shuffled complex evolution ,Evolutionary strategy ,Artificial Intelligence & Image Processing ,Mathematical Sciences ,Information and Computing Sciences ,Engineering - Abstract
Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population's capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones. © 2011 Elsevier Inc. All rights reserved.
- Published
- 2011