A new evolutionary search strategy for global optimization of high-dimensional problems

Authors:
Wei Chu;Xiaogang Gao;Soroosh Sorooshian
Affiliations:
Department of Civil and Environmental Engineering, University of California, Irvine, CA 92617, USA;Department of Civil and Environmental Engineering, University of California, Irvine, CA 92617, USA;Department of Civil and Environmental Engineering, University of California, Irvine, CA 92617, USA
Venue:
Information Sciences: an International Journal
Year:
2011

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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.