摘要
本文借助模拟物种通过环境间的迁移来适应各种自然环境这一生态现象 ,提出了解决多目标优化问题的一种新思路 :基于环境迁移模型的遗传算法 ,并且通过一个数值优化实例验证了该算法的可行性 ,与经典的多目标优化算法相比 ,有其优越性 .
Environment Transfer Genetic Algorithms(ab. ETGA) put forward in this paper is a new idea of solving Multiobjective Optimization, which simulates the biology phenomenon that species can adapt natural environment via transferring between different environments. Furthermore, an example of a multiobjective function is given to prove ETGA has advantage compared with classical Genetic Algorithms.
出处
《小型微型计算机系统》
CSCD
北大核心
2004年第1期86-88,共3页
Journal of Chinese Computer Systems
关键词
多目标优化
遗传算法
环境迁移
multiobjective optimization
genetic algorithms
environment transfer
