论文摘要
In this study, we present a Pareto-based chemicalreaction optimization（PCRO） algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.
论文目录
文章来源
类型: 期刊论文
作者: Junqing Li,Quanke Pan,Peiyong Duan,Hongyan Sang,Kaizhou Gao
来源: IEEE/CAA Journal of Automatica Sinica 2019年05期
年度: 2019
分类: 信息科技,工程科技Ⅱ辑,经济与管理科学
专业: 电力工业,自动化技术,工业经济
单位: IEEE,School of Computer Science, Liaocheng University,School of Information Science and Engineering, Shandong Normal University,School of Mechatronic Engineering and Automation, Shanghai University,Macau Institute of Systems Engineering, Macau University of Science and Technology
基金: partially supported by the National Natural Science Foundation of China(61773192,61773246,61603169,61803192),Shandong Province Higher Educational Science and Technology Program(J17KZ005),Special Fund Plan for Local Science and Technology Development Lead by Central Authority,Major Basic Research Projects in Shandong(ZR2018ZB0419)
分类号: TP18;TM73;F407.61
页码: 1240-1250
总页数: 11
文件大小: 445K
下载量: 14