An Escalating Multi-Objective DE Algorithm
In this paper, a multi-objective DE algorithm based on escalating strategy will be proposed. The main idea of this escalating strategy is to re-generate the whole evolutionary population with some technology, which results in a new population significantly indifferent from the old one while inheriting the evolutionary information from the history. By this way, the performance on global convergence can be enhanced, and premature can be avoided simultaneously. A neighborhood search procedure is imposed on some selected Pareto solutions to accelerate the evolution process for reaching a global Pareto set with well distribution. Some typical multi-objective optimization test problems are taken to solve with escalation DE and non-escalation DE respectively to verify the effectiveness of the new algorithm. The details about how to select appropriate escalating parameters and their effect on the performance of EMDE are also investigated to show that the EMDE with random flexible factor has some advantage over than that of fixed flexible factor.
multi-objective optimization de algorithm local search escalating evolution
Bin Xu Jing Yu
School of Accountancy,Central University of Finance & Economic P.R.China, Beijing, 100081 Research Center on Fictitious Economy & Data Science,CAS, P.R.China,Beijing,100190
国际会议
成都
英文
563-567
2010-07-09（万方平台首次上网日期，不代表论文的发表时间）