摘要
针对非劣分层多目标遗传(NSGA)本身所存在的局部搜索能力和易早熟的问题,鉴于模拟退火算法的局部搜索能力强和在解决易早熟问题上的优势,提出了加入局部搜索的多目标遗传算法及适用于多目标优化的模拟退火局部搜索算法和跳转准则,即在NSGA的每一代个体中的1层、2层非劣解附近进行模拟退火局部搜索.该算法能够提高非劣分层多目标遗传算法的效率,弥补了遗传算法中局部搜索能力差、易早熟的缺点.最后给出的仿真结果表明了这种算法的有效性.
The non-dominated sorting in genetic algorithms （NSGA） has some deficiencies such as the poor local search and premature convergence. An improved algorithm based on the advantage of simulated annealing is presented to overcome these shortcomings. The local search operator of simulated annealing for multiobjective optimization and the jump criteria are taken part into the new algorithm. The local search should be carried out by simulated annealing in the vicinity of the 1st and 2nd rank of non-dominant solutions. This approach can improve operational efficiency and make up for the deficiencies of NSGA. The simulation results show the effectiveness of the algorithm.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第7期921-924,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60374003)
关键词
遗传算法
多目标优化
模拟退火
小生境算子
非劣分层
genetic algorithm
multiobjective optimization
simulated annealing
niche operator
non-dominated sorting
