CAJ | 학술논문

差分演化(DE)是解决优化问题的非常有效的新兴智能算法,但它主要用于连续优化领域,至今尚不能象解决连续优化问题那样有效的处理组合优化问题.首先提出了离散DE用于组合优化问题,然后在离散DE中引入分布估计算法(EDA)来提高性能,把EDA抽样得到的全局统计信息和离散DE获得的局部演化信息相结合来产生新解,形成基于EDA的离散DE算法.为了保持种群多样性,在提出的算法中引入了位翻转变异操作.实验结果表明,EDA能大大提高离散DE的性能.
차분연화(DE)시해결우화문제적비상유효적신흥지능산법,단타주요용우련속우화영역,지금상불능상해결련속우화문제나양유효적처리조합우화문제.수선제출료리산DE용우조합우화문제,연후재리산DE중인입분포고계산법(EDA)래제고성능,파EDA추양득도적전국통계신식화리산DE획득적국부연화신식상결합래산생신해,형성기우EDA적리산DE산법.위료보지충군다양성,재제출적산법중인입료위번전변이조작.실험결과표명,EDA능대대제고리산DE적성능.
Differential evolution(DE)is the latest intelligent algorithms for solving the optimization problems very effectively.The algorithm is mainly used in solving the global continuous optimization,but their applications to combinatorial optimization have been rather limited and are not as effective as in global continuous optimization.Firstly a discrete differential evolution(DE)for combinatorial optimization is proposed,and then incorporates the estimation of distribution algorithm (EDA) into the discrete DE to improve its performance.The proposed discrete DE algorithm based on EDA combineg lobal statistical information extracted by EDA with local evolution information obtained by discrete DE to create promising solutions.In order to keep the diversities in the population,a bit flip mutation operator is also incorporated into the proposed hybrid algorithm.The results of experiment show that the EDA can significantly improve the performance of the discrete DE.