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Your search keyword '"Tan, Lijing"' showing total 5 results
Start Over Author "Tan, Lijing" Topic multi-objective optimization
5 results on '"Tan, Lijing"'

4. Multi-objective Optimization Using BFO Algorithm

Author

Niu, Ben, Wang, Hong, Tan, Lijing, Xu, Jun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Huang, De-Shuang, editor, Gan, Yong, editor, Premaratne, Prashan, editor, and Han, Kyungsook, editor

Published
2012
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5. Multi-objective bacterial foraging optimization.

Author

Niu, Ben, Wang, Hong, Wang, Jingwen, and Tan, Lijing

Subjects
*SYSTEM identification, *MATHEMATICAL optimization, *FEATURE extraction, *PROBABILITY theory, *COMPUTER simulation, *EVOLUTIONARY algorithms
Abstract

Abstract: This paper describes a novel Bacterial Foraging Optimization (BFO) approach to multi-objective optimization, called Multi-objective Bacterial Foraging Optimization (MBFO). The objectives in the Multi-objective Bacterial Foraging Optimization are maintained by a fitness survive mechanism. Bacteria with the smaller health values have the better chance to survive. Meanwhile, the main goal of multi-objective optimization problems is to obtain a superior non-dominated front which is closed to the true Pareto front. With identification of such features, the idea of integration between health sorting approach and pareto dominance mechanism are developed to search for Pareto-optimal set of problems. Moreover, strategy keeping a certain unfeasible border solutions based on a given probability is considered to improve the diversity of individuals. In addition, two different performance metrics: Diversity and Generational Distance are introduced as well to evaluate multi-objective optimization problems. Compared to two other multi-objective optimization evolutionary algorithms MOPSO and NSGA-II, simulation results show that in most cases, the proposed MBFO is able to find a much better spread of solutions and convergence to the true Pareto-optimal front faster. It suggests that MBFO is very promising in dealing with ordinary multi-objective optimization problems. [Copyright &y& Elsevier]

Published
2013
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