This paper presents a new distributed Bayesian optimization algorithm(BOA)to overcome the efficiency problem when solving NP scheduling problems.The pro-posed approach integrates BOA into the co-evolutionary schema,wh...This paper presents a new distributed Bayesian optimization algorithm(BOA)to overcome the efficiency problem when solving NP scheduling problems.The pro-posed approach integrates BOA into the co-evolutionary schema,which builds up a concurrent computing environ-ment.A new search strategy is also introduced for local op-timization process.It integrates the reinforcement learning(RL)mechanism into the BOA search processes,and then uses the mixed probability information from BOA(post-probability)and RL(pre-probability)to enhance the cooperation between different local controllers,which im-proves the optimization ability of the algorithm.The ex-periment shows that the new algorithm does better in both optimization(2.2%)and convergence(11.7%),compared with classic BOA.展开更多
基金supported by the National Edu-cation Promotion Project(No.081100601).
文摘This paper presents a new distributed Bayesian optimization algorithm(BOA)to overcome the efficiency problem when solving NP scheduling problems.The pro-posed approach integrates BOA into the co-evolutionary schema,which builds up a concurrent computing environ-ment.A new search strategy is also introduced for local op-timization process.It integrates the reinforcement learning(RL)mechanism into the BOA search processes,and then uses the mixed probability information from BOA(post-probability)and RL(pre-probability)to enhance the cooperation between different local controllers,which im-proves the optimization ability of the algorithm.The ex-periment shows that the new algorithm does better in both optimization(2.2%)and convergence(11.7%),compared with classic BOA.
