针对动态环境,提出了一种基于多智能体的进化算法(MAEA).智能体模拟生物机制特征,相互合作来寻求最优解.智能体生存于网格环境中,为了增加自身能量,智能体可以与其邻域展开竞争,并依据统计信息来获得知识进行学习.为了保持种群多样性,同时引入随机移民和对偶映射策略.通过对一系列动态优化函数的仿真实验可以得出,相比之下,基于多智能体的进化算法可以在动态环境中获得更好的性能.
In this paper,a multi-agent based evolutionary algorithm(MAEA) is presented to solve dynamic optimization problems.The agents simulate living organism features and cooperate to find the optimum.All agents live in a lattice like environment.In order to increase their energy,agents can compete with their neighbors and acquire knowledge based on statistic information.In order to maintain the diversity of the population,the random immigrants and the adaptive dual mapping schemes are used.Simulation experiments ...