基于环境经济调度问题的空间自适应划分多目标优化

Environmental economic power dispatch based on multi-objective evolution algorithm with adaptive space partition

提出一种基于空间自适应划分的多目标优化算法.为了增强种群的收敛性和多样性,多维搜索空间被划分成多个网格,网格内的粒子通过共享"引导"粒子的经验信息调整自身的速度和位置,并引入年龄观测器实时记录引导粒子对Pareto解集所做的贡献,及时更新引导粒子,以增强算法的全局搜索能力.对多目标测试函数以及环境经济调度问题进行了仿真实验,实验结果表明,所提出算法能对解空间进行更加全面、充分的探索,快速找到一组分布具有较好的逼近性、宽广性和均匀性的最优解集合.

A multi-objective optimization algorithm based on adaptive spatial division is proposed to solve the environmental/economic dispatch problem.As to keep the population diversity,the search space is divided into multiple regions,particles are guided by three kinds of local and global particles to rapidly near the Pareto optimal frontier,an age observer is used to record the contribution of guiders for particles near the Pareto optimal solution set real-timely,and guiders are changed in a certain cycle according to the contribution degree.The algorithm can fully explore the solution space,so as to quickly find a set of distribution with the best possible approximation.The experiment simulations on the international test function and the power system environment economic dispatch model are carried out.The results show that the improved algorithm can maintain the diversity of Pareto-optimal solutions and get better convergence at the same time.