摘要
提出一种新的交互式 Multi-Agent 遗传算法.该算法使固定在网格上的相邻智能体之间进行交叉、变异、死亡与再生操作和最优智能体本身进行自学习,来提高智能体的能量,从而使得算法获得较强的全局收敛能力和局部搜索能力.用户在每代进化中,只需选择感兴趣的个体,而不用评价每个个体的适应值,使得用户的评价操作变得简单易行.函数优化和服装设计的仿真实验表明算法能以较快的进化速度收敛,并使用户总评价次数减少,从而有效缓解用户的疲劳.
A interactive multi-agent genetic algorithm （IMAGA） is proposed. Every agent fixed on a lattice-point in IMAGA interoperates with their neighbors, and the optimal one carries out self- learning to increase the energy. Hence the abilities of global convergence and local search of the algorithm are improved. In every generation, users only need to select the interested individuals instead of evaluating every individual, which simplifies the users＂ evaluation. The simulations of function optimization and fashion design show that the proposed algorithm with higher convergence velocity reduces the total times of users＂ evaluation so as to alleviate user fatigue.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2007年第3期308-312,共5页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.70471046)
高等学校博士学科点专项科研基金(No.20050359006
20040359004)
关键词
多智能体
交互式遗传算法
用户疲劳
服装设计
Multi-Agent, Interactive Genetic Algorithm, User Fatigue, Fashion Design
