GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space
Weihang Zhu, Yaohang Li · 2010
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.