Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data

doi:10.1109/TII.2017.2677939

	Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data
	Lin, Fan 1; Zeng, Jiasong 1; Xiahou, Jianbing 1; Wang, Beizhan 1; Zeng, Wenhua 1; Lv, Haibin 2
刊名	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
	2017-08
卷号	13 期号:4 页码:1979-1988
关键词	Big data multiobjective optimization non-dominated sorting and bidirectional local search (NSBLS)
ISSN号	1551-3203
DOI	10.1109/TII.2017.2677939
英文摘要	The improved differential evolutionary algorithm (EA) discussed in this paper is used to solve high-dimensional big data. Specifically, the algorithm improves population diversity by expanding the searching scope of the population, prevents premature deaths of the population through wider and more specific searches, and aims to solve the high-dimensional issue. To achieve this improvement goal, the paper suggests a multilayer hierarchical architecture on the basis of the above-mentioned heuristic mechanism. In each layer of the hierarchical architecture in the dynamic subpopulation, individuals who are more suitable for isolated evolution can better coexist with the original main population. We propose a new multiobjective optimization algorithm based on nondominated sorting and bidirectional local search (NSBLS). The algorithm takes the local beam search as the main body. NSBLS outputs the nondominated solution set through a continuous iterative search when the iteration termination condition is satisfied. It is worthy to note that the iteration of NSBLS is similar to the generation of the EA; therefore, this paper uses generation to represent the iterations. An algorithm introduces a new distribution maintaining strategy based on the sampling theory to combine with the fast nondominated sorting algorithm in order to select a new population into the next iteration. NSBLS will compare with three classical algorithms: NSGA-II, MOEA/D-DE, and MODEA through a series of bi-objective test problems. The proposed nondominated sorting and local search is able to find a better spread of solutions and better convergence to the true Pareto-optimal front compared to the other four algorithms. The outstanding performance of the proposed technology was proven in well-known benchmark problems.
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000406933400049
内容类型	期刊论文
源URL	[http://ir.fio.com.cn/handle/2SI8HI0U/3125]
专题	自然资源部第一海洋研究所
作者单位	1.Xiamen Univ, Sch Software Engn, Xiamen 361000, Peoples R China; 2.State Ocean Adm, Qingdao Huanhai Marine Engn Prospecting Inst, Qingdao 266100, Peoples R China
推荐引用方式 GB/T 7714	Lin, Fan,Zeng, Jiasong,Xiahou, Jianbing,et al. Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2017,13(4):1979-1988.
APA	Lin, Fan,Zeng, Jiasong,Xiahou, Jianbing,Wang, Beizhan,Zeng, Wenhua,&Lv, Haibin.(2017).Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,13(4),1979-1988.
MLA	Lin, Fan,et al."Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 13.4(2017):1979-1988.