知识引导的多目标多智能体进化算法

将智能体模型与知识模型相结合,提出一种知识引导的多目标多智能体进化算法.算法定义了智能体的不同邻域环境,并通过对邻域之间的竞争、正交交叉、知识学习等操作实现种群的演化过程.算法采用一种新颖的方法求非劣解集,并使用循环拥挤排序法对外部归档集进行维护.通过对多个测试函数的仿真结果表明,知识的引入不仅增加了种群多样性,而且提高了算法的收敛性.

Knowledge-guided multi-objective multi-agent evolutionary algorithm

Combining the agent model and the knowledge model, we propose a knowledge-guided multi-objective multi-agent evolutionary algorithm. Different kinds of neighbor environments of the agent are defined firstly. And then the population evolution process is realized through three operators named competition, orthogonal crossover and knowledge learning in the proposed algorithm. A novel method for finding the non-dominated sets is developed and the circular crowded sorting method is adopted to maintain the external archive-set in the proposed algorithm. Simulation results of several benchmark functions show that the knowledge-guided multi-objective multi-agent evolutionary algorithm not onlyincreases the diversity of the population but also improves the convergence of the algorithm.