GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space

Authors:
Weihang Zhu;Yaohang Li
Affiliations:
Lamar University, Beaumont, TX, USA;North Carolina A&T State University, Greensboro, NC, USA
Venue:
Proceedings of the 2nd workshop on Bio-inspired algorithms for distributed systems
Year:
2010

Citing 5
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Abstract

In this paper, the attractive features of evolutionary algorithm and Markov Chain Monte Carlo are combined into a new Differential Evolutionary Markov Chain Monte Carlo (DE-MCMC) method for multi-objective optimization problems with continuous variables. The DE-MCMC evolves a population of Markov chains through differential evolution (DE) toward a diversified set of solutions at the Pareto optimal front in the multi-objective function space. The computational results show the effectiveness of the DE-MCMC algorithm in a variety of standardized test functions as well as a protein loop structure sampling application. Moreover, the DE-MCMC algorithm can efficiently take advantage of the massive-parallel, many-core architecture, where its implementation on GPU can achieve speedup of 14~35.